mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
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// SPDX-License-Identifier: GPL-2.0
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/*
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* Data Access Monitor
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*
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* Author: SeongJae Park <sjpark@amazon.de>
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*/
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#define pr_fmt(fmt) "damon: " fmt
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#include <linux/damon.h>
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#include <linux/delay.h>
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#include <linux/kthread.h>
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mm/damon/schemes: activate schemes based on a watermarks mechanism
DAMON-based operation schemes need to be manually turned on and off. In
some use cases, however, the condition for turning a scheme on and off
would depend on the system's situation. For example, schemes for
proactive pages reclamation would need to be turned on when some memory
pressure is detected, and turned off when the system has enough free
memory.
For easier control of schemes activation based on the system situation,
this introduces a watermarks-based mechanism. The client can describe
the watermark metric (e.g., amount of free memory in the system),
watermark check interval, and three watermarks, namely high, mid, and
low. If the scheme is deactivated, it only gets the metric and compare
that to the three watermarks for every check interval. If the metric is
higher than the high watermark, the scheme is deactivated. If the
metric is between the mid watermark and the low watermark, the scheme is
activated. If the metric is lower than the low watermark, the scheme is
deactivated again. This is to allow users fall back to traditional
page-granularity mechanisms.
Link: https://lkml.kernel.org/r/20211019150731.16699-12-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:47:47 +00:00
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#include <linux/mm.h>
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mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
#include <linux/slab.h>
|
2021-11-05 20:47:33 +00:00
|
|
|
#include <linux/string.h>
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
|
2021-09-08 02:56:48 +00:00
|
|
|
#define CREATE_TRACE_POINTS
|
|
|
|
#include <trace/events/damon.h>
|
|
|
|
|
2021-09-08 02:57:09 +00:00
|
|
|
#ifdef CONFIG_DAMON_KUNIT_TEST
|
|
|
|
#undef DAMON_MIN_REGION
|
|
|
|
#define DAMON_MIN_REGION 1
|
|
|
|
#endif
|
|
|
|
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
static DEFINE_MUTEX(damon_lock);
|
|
|
|
static int nr_running_ctxs;
|
mm/damon/core: allow non-exclusive DAMON start/stop
Patch series "Introduce DAMON sysfs interface", v3.
Introduction
============
DAMON's debugfs-based user interface (DAMON_DBGFS) served very well, so
far. However, it unnecessarily depends on debugfs, while DAMON is not
aimed to be used for only debugging. Also, the interface receives
multiple values via one file. For example, schemes file receives 18
values. As a result, it is inefficient, hard to be used, and difficult to
be extended. Especially, keeping backward compatibility of user space
tools is getting only challenging. It would be better to implement
another reliable and flexible interface and deprecate DAMON_DBGFS in long
term.
For the reason, this patchset introduces a sysfs-based new user interface
of DAMON. The idea of the new interface is, using directory hierarchies
and having one dedicated file for each value. For a short example, users
can do the virtual address monitoring via the interface as below:
# cd /sys/kernel/mm/damon/admin/
# echo 1 > kdamonds/nr_kdamonds
# echo 1 > kdamonds/0/contexts/nr_contexts
# echo vaddr > kdamonds/0/contexts/0/operations
# echo 1 > kdamonds/0/contexts/0/targets/nr_targets
# echo $(pidof <workload>) > kdamonds/0/contexts/0/targets/0/pid_target
# echo on > kdamonds/0/state
A brief representation of the files hierarchy of DAMON sysfs interface is
as below. Childs are represented with indentation, directories are having
'/' suffix, and files in each directory are separated by comma.
/sys/kernel/mm/damon/admin
│ kdamonds/nr_kdamonds
│ │ 0/state,pid
│ │ │ contexts/nr_contexts
│ │ │ │ 0/operations
│ │ │ │ │ monitoring_attrs/
│ │ │ │ │ │ intervals/sample_us,aggr_us,update_us
│ │ │ │ │ │ nr_regions/min,max
│ │ │ │ │ targets/nr_targets
│ │ │ │ │ │ 0/pid_target
│ │ │ │ │ │ │ regions/nr_regions
│ │ │ │ │ │ │ │ 0/start,end
│ │ │ │ │ │ │ │ ...
│ │ │ │ │ │ ...
│ │ │ │ │ schemes/nr_schemes
│ │ │ │ │ │ 0/action
│ │ │ │ │ │ │ access_pattern/
│ │ │ │ │ │ │ │ sz/min,max
│ │ │ │ │ │ │ │ nr_accesses/min,max
│ │ │ │ │ │ │ │ age/min,max
│ │ │ │ │ │ │ quotas/ms,bytes,reset_interval_ms
│ │ │ │ │ │ │ │ weights/sz_permil,nr_accesses_permil,age_permil
│ │ │ │ │ │ │ watermarks/metric,interval_us,high,mid,low
│ │ │ │ │ │ │ stats/nr_tried,sz_tried,nr_applied,sz_applied,qt_exceeds
│ │ │ │ │ │ ...
│ │ │ │ ...
│ │ ...
Detailed usage of the files will be described in the final Documentation
patch of this patchset.
Main Difference Between DAMON_DBGFS and DAMON_SYSFS
---------------------------------------------------
At the moment, DAMON_DBGFS and DAMON_SYSFS provides same features. One
important difference between them is their exclusiveness. DAMON_DBGFS
works in an exclusive manner, so that no DAMON worker thread (kdamond) in
the system can run concurrently and interfere somehow. For the reason,
DAMON_DBGFS asks users to construct all monitoring contexts and start them
at once. It's not a big problem but makes the operation a little bit
complex and unflexible.
For more flexible usage, DAMON_SYSFS moves the responsibility of
preventing any possible interference to the admins and work in a
non-exclusive manner. That is, users can configure and start contexts one
by one. Note that DAMON respects both exclusive groups and non-exclusive
groups of contexts, in a manner similar to that of reader-writer locks.
That is, if any exclusive monitoring contexts (e.g., contexts that started
via DAMON_DBGFS) are running, DAMON_SYSFS does not start new contexts, and
vice versa.
Future Plan of DAMON_DBGFS Deprecation
======================================
Once this patchset is merged, DAMON_DBGFS development will be frozen.
That is, we will maintain it to work as is now so that no users will be
break. But, it will not be extended to provide any new feature of DAMON.
The support will be continued only until next LTS release. After that, we
will drop DAMON_DBGFS.
User-space Tooling Compatibility
--------------------------------
As DAMON_SYSFS provides all features of DAMON_DBGFS, all user space
tooling can move to DAMON_SYSFS. As we will continue supporting
DAMON_DBGFS until next LTS kernel release, user space tools would have
enough time to move to DAMON_SYSFS.
The official user space tool, damo[1], is already supporting both
DAMON_SYSFS and DAMON_DBGFS. Both correctness tests[2] and performance
tests[3] of DAMON using DAMON_SYSFS also passed.
[1] https://github.com/awslabs/damo
[2] https://github.com/awslabs/damon-tests/tree/master/corr
[3] https://github.com/awslabs/damon-tests/tree/master/perf
Sequence of Patches
===================
First two patches (patches 1-2) make core changes for DAMON_SYSFS. The
first one (patch 1) allows non-exclusive DAMON contexts so that
DAMON_SYSFS can work in non-exclusive mode, while the second one (patch 2)
adds size of DAMON enum types so that DAMON API users can safely iterate
the enums.
Third patch (patch 3) implements basic sysfs stub for virtual address
spaces monitoring. Note that this implements only sysfs files and DAMON
is not linked. Fourth patch (patch 4) links the DAMON_SYSFS to DAMON so
that users can control DAMON using the sysfs files.
Following six patches (patches 5-10) implements other DAMON features that
DAMON_DBGFS supports one by one (physical address space monitoring,
DAMON-based operation schemes, schemes quotas, schemes prioritization
weights, schemes watermarks, and schemes stats).
Following patch (patch 11) adds a simple selftest for DAMON_SYSFS, and the
final one (patch 12) documents DAMON_SYSFS.
This patch (of 13):
To avoid interference between DAMON contexts monitoring overlapping memory
regions, damon_start() works in an exclusive manner. That is,
damon_start() does nothing bug fails if any context that started by
another instance of the function is still running. This makes its usage a
little bit restrictive. However, admins could aware each DAMON usage and
address such interferences on their own in some cases.
This commit hence implements non-exclusive mode of the function and allows
the callers to select the mode. Note that the exclusive groups and
non-exclusive groups of contexts will respect each other in a manner
similar to that of reader-writer locks. Therefore, this commit will not
cause any behavioral change to the exclusive groups.
Link: https://lkml.kernel.org/r/20220228081314.5770-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20220228081314.5770-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <skhan@linuxfoundation.org>
Cc: David Rientjes <rientjes@google.com>
Cc: Xin Hao <xhao@linux.alibaba.com>
Cc: Greg Kroah-Hartman <gregkh@linuxfoundation.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-22 21:49:21 +00:00
|
|
|
static bool running_exclusive_ctxs;
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
|
2022-03-22 21:48:49 +00:00
|
|
|
static DEFINE_MUTEX(damon_ops_lock);
|
|
|
|
static struct damon_operations damon_registered_ops[NR_DAMON_OPS];
|
|
|
|
|
2022-09-12 14:39:03 +00:00
|
|
|
static struct kmem_cache *damon_region_cache __ro_after_init;
|
|
|
|
|
2022-03-22 21:48:49 +00:00
|
|
|
/* Should be called under damon_ops_lock with id smaller than NR_DAMON_OPS */
|
2022-05-10 01:20:51 +00:00
|
|
|
static bool __damon_is_registered_ops(enum damon_ops_id id)
|
2022-03-22 21:48:49 +00:00
|
|
|
{
|
|
|
|
struct damon_operations empty_ops = {};
|
|
|
|
|
|
|
|
if (!memcmp(&empty_ops, &damon_registered_ops[id], sizeof(empty_ops)))
|
|
|
|
return false;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
2022-05-10 01:20:51 +00:00
|
|
|
/**
|
|
|
|
* damon_is_registered_ops() - Check if a given damon_operations is registered.
|
|
|
|
* @id: Id of the damon_operations to check if registered.
|
|
|
|
*
|
|
|
|
* Return: true if the ops is set, false otherwise.
|
|
|
|
*/
|
|
|
|
bool damon_is_registered_ops(enum damon_ops_id id)
|
|
|
|
{
|
|
|
|
bool registered;
|
|
|
|
|
|
|
|
if (id >= NR_DAMON_OPS)
|
|
|
|
return false;
|
|
|
|
mutex_lock(&damon_ops_lock);
|
|
|
|
registered = __damon_is_registered_ops(id);
|
|
|
|
mutex_unlock(&damon_ops_lock);
|
|
|
|
return registered;
|
|
|
|
}
|
|
|
|
|
2022-03-22 21:48:49 +00:00
|
|
|
/**
|
|
|
|
* damon_register_ops() - Register a monitoring operations set to DAMON.
|
|
|
|
* @ops: monitoring operations set to register.
|
|
|
|
*
|
|
|
|
* This function registers a monitoring operations set of valid &struct
|
|
|
|
* damon_operations->id so that others can find and use them later.
|
|
|
|
*
|
|
|
|
* Return: 0 on success, negative error code otherwise.
|
|
|
|
*/
|
|
|
|
int damon_register_ops(struct damon_operations *ops)
|
|
|
|
{
|
|
|
|
int err = 0;
|
|
|
|
|
|
|
|
if (ops->id >= NR_DAMON_OPS)
|
|
|
|
return -EINVAL;
|
|
|
|
mutex_lock(&damon_ops_lock);
|
|
|
|
/* Fail for already registered ops */
|
2022-05-10 01:20:51 +00:00
|
|
|
if (__damon_is_registered_ops(ops->id)) {
|
2022-03-22 21:48:49 +00:00
|
|
|
err = -EINVAL;
|
|
|
|
goto out;
|
|
|
|
}
|
|
|
|
damon_registered_ops[ops->id] = *ops;
|
|
|
|
out:
|
|
|
|
mutex_unlock(&damon_ops_lock);
|
|
|
|
return err;
|
|
|
|
}
|
|
|
|
|
|
|
|
/**
|
|
|
|
* damon_select_ops() - Select a monitoring operations to use with the context.
|
|
|
|
* @ctx: monitoring context to use the operations.
|
|
|
|
* @id: id of the registered monitoring operations to select.
|
|
|
|
*
|
|
|
|
* This function finds registered monitoring operations set of @id and make
|
|
|
|
* @ctx to use it.
|
|
|
|
*
|
|
|
|
* Return: 0 on success, negative error code otherwise.
|
|
|
|
*/
|
|
|
|
int damon_select_ops(struct damon_ctx *ctx, enum damon_ops_id id)
|
|
|
|
{
|
|
|
|
int err = 0;
|
|
|
|
|
|
|
|
if (id >= NR_DAMON_OPS)
|
|
|
|
return -EINVAL;
|
|
|
|
|
|
|
|
mutex_lock(&damon_ops_lock);
|
2022-05-10 01:20:51 +00:00
|
|
|
if (!__damon_is_registered_ops(id))
|
2022-03-22 21:48:49 +00:00
|
|
|
err = -EINVAL;
|
|
|
|
else
|
|
|
|
ctx->ops = damon_registered_ops[id];
|
|
|
|
mutex_unlock(&damon_ops_lock);
|
|
|
|
return err;
|
|
|
|
}
|
|
|
|
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
/*
|
|
|
|
* Construct a damon_region struct
|
|
|
|
*
|
|
|
|
* Returns the pointer to the new struct if success, or NULL otherwise
|
|
|
|
*/
|
|
|
|
struct damon_region *damon_new_region(unsigned long start, unsigned long end)
|
|
|
|
{
|
|
|
|
struct damon_region *region;
|
|
|
|
|
2022-09-12 14:39:03 +00:00
|
|
|
region = kmem_cache_alloc(damon_region_cache, GFP_KERNEL);
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
if (!region)
|
|
|
|
return NULL;
|
|
|
|
|
|
|
|
region->ar.start = start;
|
|
|
|
region->ar.end = end;
|
|
|
|
region->nr_accesses = 0;
|
2023-09-15 02:52:48 +00:00
|
|
|
region->nr_accesses_bp = 0;
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
INIT_LIST_HEAD(®ion->list);
|
|
|
|
|
mm/damon/core: account age of target regions
Patch series "Implement Data Access Monitoring-based Memory Operation Schemes".
Introduction
============
DAMON[1] can be used as a primitive for data access aware memory
management optimizations. For that, users who want such optimizations
should run DAMON, read the monitoring results, analyze it, plan a new
memory management scheme, and apply the new scheme by themselves. Such
efforts will be inevitable for some complicated optimizations.
However, in many other cases, the users would simply want the system to
apply a memory management action to a memory region of a specific size
having a specific access frequency for a specific time. For example,
"page out a memory region larger than 100 MiB keeping only rare accesses
more than 2 minutes", or "Do not use THP for a memory region larger than
2 MiB rarely accessed for more than 1 seconds".
To make the works easier and non-redundant, this patchset implements a
new feature of DAMON, which is called Data Access Monitoring-based
Operation Schemes (DAMOS). Using the feature, users can describe the
normal schemes in a simple way and ask DAMON to execute those on its
own.
[1] https://damonitor.github.io
Evaluations
===========
DAMOS is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation
are not for production but only for proof of concepts.
Please refer to the showcase web site's evaluation document[1] for
detailed evaluation setup and results.
[1] https://damonitor.github.io/doc/html/v34/vm/damon/eval.html
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are
another couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://git.kernel.org/sj/h/damon/for-v5.4.y
- For v5.10.y: https://git.kernel.org/sj/h/damon/for-v5.10.y
Sequence Of Patches
===================
The 1st patch accounts age of each region. The 2nd patch implements the
core of the DAMON-based operation schemes feature. The 3rd patch makes
the default monitoring primitives for virtual address spaces to support
the schemes. From this point, the kernel space users can use DAMOS.
The 4th patch exports the feature to the user space via the debugfs
interface. The 5th patch implements schemes statistics feature for
easier tuning of the schemes and runtime access pattern analysis, and
the 6th patch adds selftests for these changes. Finally, the 7th patch
documents this new feature.
This patch (of 7):
DAMON can be used for data access pattern aware memory management
optimizations. For that, users should run DAMON, read the monitoring
results, analyze it, plan a new memory management scheme, and apply the
new scheme by themselves. It would not be too hard, but still require
some level of effort. For complicated cases, this effort is inevitable.
That said, in many cases, users would simply want to apply an actions to
a memory region of a specific size having a specific access frequency
for a specific time. For example, "page out a memory region larger than
100 MiB but having a low access frequency more than 10 minutes", or "Use
THP for a memory region larger than 2 MiB having a high access frequency
for more than 2 seconds".
For such optimizations, users will need to first account the age of each
region themselves. To reduce such efforts, this implements a simple age
account of each region in DAMON. For each aggregation step, DAMON
compares the access frequency with that from last aggregation and reset
the age of the region if the change is significant. Else, the age is
incremented. Also, in case of the merge of regions, the region
size-weighted average of the ages is set as the age of merged new
region.
Link: https://lkml.kernel.org/r/20211001125604.29660-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20211001125604.29660-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Greg Thelen <gthelen@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: David Rienjes <rientjes@google.com>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:18 +00:00
|
|
|
region->age = 0;
|
|
|
|
region->last_nr_accesses = 0;
|
|
|
|
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
return region;
|
|
|
|
}
|
|
|
|
|
|
|
|
void damon_add_region(struct damon_region *r, struct damon_target *t)
|
|
|
|
{
|
|
|
|
list_add_tail(&r->list, &t->regions_list);
|
2021-09-08 02:56:36 +00:00
|
|
|
t->nr_regions++;
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
}
|
|
|
|
|
2021-09-08 02:56:36 +00:00
|
|
|
static void damon_del_region(struct damon_region *r, struct damon_target *t)
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
{
|
|
|
|
list_del(&r->list);
|
2021-09-08 02:56:36 +00:00
|
|
|
t->nr_regions--;
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
}
|
|
|
|
|
|
|
|
static void damon_free_region(struct damon_region *r)
|
|
|
|
{
|
2022-09-12 14:39:03 +00:00
|
|
|
kmem_cache_free(damon_region_cache, r);
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
}
|
|
|
|
|
2021-09-08 02:56:36 +00:00
|
|
|
void damon_destroy_region(struct damon_region *r, struct damon_target *t)
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
{
|
2021-09-08 02:56:36 +00:00
|
|
|
damon_del_region(r, t);
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
damon_free_region(r);
|
|
|
|
}
|
|
|
|
|
2022-05-10 01:20:55 +00:00
|
|
|
/*
|
|
|
|
* Check whether a region is intersecting an address range
|
|
|
|
*
|
|
|
|
* Returns true if it is.
|
|
|
|
*/
|
|
|
|
static bool damon_intersect(struct damon_region *r,
|
|
|
|
struct damon_addr_range *re)
|
|
|
|
{
|
|
|
|
return !(r->ar.end <= re->start || re->end <= r->ar.start);
|
|
|
|
}
|
|
|
|
|
2022-09-09 20:28:56 +00:00
|
|
|
/*
|
|
|
|
* Fill holes in regions with new regions.
|
|
|
|
*/
|
|
|
|
static int damon_fill_regions_holes(struct damon_region *first,
|
|
|
|
struct damon_region *last, struct damon_target *t)
|
|
|
|
{
|
|
|
|
struct damon_region *r = first;
|
|
|
|
|
|
|
|
damon_for_each_region_from(r, t) {
|
|
|
|
struct damon_region *next, *newr;
|
|
|
|
|
|
|
|
if (r == last)
|
|
|
|
break;
|
|
|
|
next = damon_next_region(r);
|
|
|
|
if (r->ar.end != next->ar.start) {
|
|
|
|
newr = damon_new_region(r->ar.end, next->ar.start);
|
|
|
|
if (!newr)
|
|
|
|
return -ENOMEM;
|
|
|
|
damon_insert_region(newr, r, next, t);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
return 0;
|
|
|
|
}
|
|
|
|
|
2022-05-10 01:20:55 +00:00
|
|
|
/*
|
|
|
|
* damon_set_regions() - Set regions of a target for given address ranges.
|
|
|
|
* @t: the given target.
|
|
|
|
* @ranges: array of new monitoring target ranges.
|
|
|
|
* @nr_ranges: length of @ranges.
|
|
|
|
*
|
|
|
|
* This function adds new regions to, or modify existing regions of a
|
|
|
|
* monitoring target to fit in specific ranges.
|
|
|
|
*
|
|
|
|
* Return: 0 if success, or negative error code otherwise.
|
|
|
|
*/
|
|
|
|
int damon_set_regions(struct damon_target *t, struct damon_addr_range *ranges,
|
|
|
|
unsigned int nr_ranges)
|
|
|
|
{
|
|
|
|
struct damon_region *r, *next;
|
|
|
|
unsigned int i;
|
2022-09-09 20:28:56 +00:00
|
|
|
int err;
|
2022-05-10 01:20:55 +00:00
|
|
|
|
|
|
|
/* Remove regions which are not in the new ranges */
|
|
|
|
damon_for_each_region_safe(r, next, t) {
|
|
|
|
for (i = 0; i < nr_ranges; i++) {
|
|
|
|
if (damon_intersect(r, &ranges[i]))
|
|
|
|
break;
|
|
|
|
}
|
|
|
|
if (i == nr_ranges)
|
|
|
|
damon_destroy_region(r, t);
|
|
|
|
}
|
|
|
|
|
2022-09-06 15:18:47 +00:00
|
|
|
r = damon_first_region(t);
|
2022-05-10 01:20:55 +00:00
|
|
|
/* Add new regions or resize existing regions to fit in the ranges */
|
|
|
|
for (i = 0; i < nr_ranges; i++) {
|
|
|
|
struct damon_region *first = NULL, *last, *newr;
|
|
|
|
struct damon_addr_range *range;
|
|
|
|
|
|
|
|
range = &ranges[i];
|
|
|
|
/* Get the first/last regions intersecting with the range */
|
2022-09-06 15:18:47 +00:00
|
|
|
damon_for_each_region_from(r, t) {
|
2022-05-10 01:20:55 +00:00
|
|
|
if (damon_intersect(r, range)) {
|
|
|
|
if (!first)
|
|
|
|
first = r;
|
|
|
|
last = r;
|
|
|
|
}
|
|
|
|
if (r->ar.start >= range->end)
|
|
|
|
break;
|
|
|
|
}
|
|
|
|
if (!first) {
|
|
|
|
/* no region intersects with this range */
|
|
|
|
newr = damon_new_region(
|
|
|
|
ALIGN_DOWN(range->start,
|
|
|
|
DAMON_MIN_REGION),
|
|
|
|
ALIGN(range->end, DAMON_MIN_REGION));
|
|
|
|
if (!newr)
|
|
|
|
return -ENOMEM;
|
|
|
|
damon_insert_region(newr, damon_prev_region(r), r, t);
|
|
|
|
} else {
|
|
|
|
/* resize intersecting regions to fit in this range */
|
|
|
|
first->ar.start = ALIGN_DOWN(range->start,
|
|
|
|
DAMON_MIN_REGION);
|
|
|
|
last->ar.end = ALIGN(range->end, DAMON_MIN_REGION);
|
2022-09-09 20:28:56 +00:00
|
|
|
|
|
|
|
/* fill possible holes in the range */
|
|
|
|
err = damon_fill_regions_holes(first, last, t);
|
|
|
|
if (err)
|
|
|
|
return err;
|
2022-05-10 01:20:55 +00:00
|
|
|
}
|
|
|
|
}
|
|
|
|
return 0;
|
|
|
|
}
|
|
|
|
|
mm/damon/core: implement damos filter
Patch series "implement DAMOS filtering for anon pages and/or specific
memory cgroups"
DAMOS let users do system operations in a data access pattern oriented
way. The data access pattern, which is extracted by DAMON, is somewhat
accurate more than what user space could know in many cases. However, in
some situation, users could know something more than the kernel about the
pattern or some special requirements for some types of memory or
processes. For example, some users would have slow swap devices and knows
latency-ciritical processes and therefore want to use DAMON-based
proactive reclamation (DAMON_RECLAIM) for only non-anonymous pages of
non-latency-critical processes.
For such restriction, users could exclude the memory regions from the
initial monitoring regions and use non-dynamic monitoring regions update
monitoring operations set including fvaddr and paddr. They could also
adjust the DAMOS target access pattern. For dynamically changing memory
layout and access pattern, those would be not enough.
To help the case, add an interface, namely DAMOS filters, which can be
used to avoid the DAMOS actions be applied to specific types of memory, to
DAMON kernel API (damon.h). At the moment, it supports filtering
anonymous pages and/or specific memory cgroups in or out for each DAMOS
scheme.
This patchset adds the support for all DAMOS actions that 'paddr'
monitoring operations set supports ('pageout', 'lru_prio', and
'lru_deprio'), and the functionality is exposed via DAMON kernel API
(damon.h) the DAMON sysfs interface (/sys/kernel/mm/damon/admins/), and
DAMON_RECLAIM module parameters.
Patches Sequence
----------------
First patch implements DAMOS filter interface to DAMON kernel API. Second
patch makes the physical address space monitoring operations set to
support the filters from all supporting DAMOS actions. Third patch adds
anonymous pages filter support to DAMON_RECLAIM, and the fourth patch
documents the DAMON_RECLAIM's new feature. Fifth to seventh patches
implement DAMON sysfs files for support of the filters, and eighth patch
connects the file to use DAMOS filters feature. Ninth patch adds simple
self test cases for DAMOS filters of the sysfs interface. Finally,
following two patches (tenth and eleventh) document the new features and
interfaces.
This patch (of 11):
DAMOS lets users do system operation in a data access pattern oriented
way. The data access pattern, which is extracted by DAMON, is somewhat
accurate more than what user space could know in many cases. However, in
some situation, users could know something more than the kernel about the
pattern or some special requirements for some types of memory or
processes. For example, some users would have slow swap devices and knows
latency-ciritical processes and therefore want to use DAMON-based
proactive reclamation (DAMON_RECLAIM) for only non-anonymous pages of
non-latency-critical processes.
For such restriction, users could exclude the memory regions from the
initial monitoring regions and use non-dynamic monitoring regions update
monitoring operations set including fvaddr and paddr. They could also
adjust the DAMOS target access pattern. For dynamically changing memory
layout and access pattern, those would be not enough.
To help the case, add an interface, namely DAMOS filters, which can be
used to avoid the DAMOS actions be applied to specific types of memory, to
DAMON kernel API (damon.h). At the moment, it supports filtering
anonymous pages and/or specific memory cgroups in or out for each DAMOS
scheme.
Note that this commit adds only the interface to the DAMON kernel API.
The impelmentation should be made in the monitoring operations sets, and
following commits will add that.
Link: https://lkml.kernel.org/r/20221205230830.144349-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20221205230830.144349-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2022-12-05 23:08:20 +00:00
|
|
|
struct damos_filter *damos_new_filter(enum damos_filter_type type,
|
|
|
|
bool matching)
|
|
|
|
{
|
|
|
|
struct damos_filter *filter;
|
|
|
|
|
|
|
|
filter = kmalloc(sizeof(*filter), GFP_KERNEL);
|
|
|
|
if (!filter)
|
|
|
|
return NULL;
|
|
|
|
filter->type = type;
|
|
|
|
filter->matching = matching;
|
2023-07-29 20:37:32 +00:00
|
|
|
INIT_LIST_HEAD(&filter->list);
|
mm/damon/core: implement damos filter
Patch series "implement DAMOS filtering for anon pages and/or specific
memory cgroups"
DAMOS let users do system operations in a data access pattern oriented
way. The data access pattern, which is extracted by DAMON, is somewhat
accurate more than what user space could know in many cases. However, in
some situation, users could know something more than the kernel about the
pattern or some special requirements for some types of memory or
processes. For example, some users would have slow swap devices and knows
latency-ciritical processes and therefore want to use DAMON-based
proactive reclamation (DAMON_RECLAIM) for only non-anonymous pages of
non-latency-critical processes.
For such restriction, users could exclude the memory regions from the
initial monitoring regions and use non-dynamic monitoring regions update
monitoring operations set including fvaddr and paddr. They could also
adjust the DAMOS target access pattern. For dynamically changing memory
layout and access pattern, those would be not enough.
To help the case, add an interface, namely DAMOS filters, which can be
used to avoid the DAMOS actions be applied to specific types of memory, to
DAMON kernel API (damon.h). At the moment, it supports filtering
anonymous pages and/or specific memory cgroups in or out for each DAMOS
scheme.
This patchset adds the support for all DAMOS actions that 'paddr'
monitoring operations set supports ('pageout', 'lru_prio', and
'lru_deprio'), and the functionality is exposed via DAMON kernel API
(damon.h) the DAMON sysfs interface (/sys/kernel/mm/damon/admins/), and
DAMON_RECLAIM module parameters.
Patches Sequence
----------------
First patch implements DAMOS filter interface to DAMON kernel API. Second
patch makes the physical address space monitoring operations set to
support the filters from all supporting DAMOS actions. Third patch adds
anonymous pages filter support to DAMON_RECLAIM, and the fourth patch
documents the DAMON_RECLAIM's new feature. Fifth to seventh patches
implement DAMON sysfs files for support of the filters, and eighth patch
connects the file to use DAMOS filters feature. Ninth patch adds simple
self test cases for DAMOS filters of the sysfs interface. Finally,
following two patches (tenth and eleventh) document the new features and
interfaces.
This patch (of 11):
DAMOS lets users do system operation in a data access pattern oriented
way. The data access pattern, which is extracted by DAMON, is somewhat
accurate more than what user space could know in many cases. However, in
some situation, users could know something more than the kernel about the
pattern or some special requirements for some types of memory or
processes. For example, some users would have slow swap devices and knows
latency-ciritical processes and therefore want to use DAMON-based
proactive reclamation (DAMON_RECLAIM) for only non-anonymous pages of
non-latency-critical processes.
For such restriction, users could exclude the memory regions from the
initial monitoring regions and use non-dynamic monitoring regions update
monitoring operations set including fvaddr and paddr. They could also
adjust the DAMOS target access pattern. For dynamically changing memory
layout and access pattern, those would be not enough.
To help the case, add an interface, namely DAMOS filters, which can be
used to avoid the DAMOS actions be applied to specific types of memory, to
DAMON kernel API (damon.h). At the moment, it supports filtering
anonymous pages and/or specific memory cgroups in or out for each DAMOS
scheme.
Note that this commit adds only the interface to the DAMON kernel API.
The impelmentation should be made in the monitoring operations sets, and
following commits will add that.
Link: https://lkml.kernel.org/r/20221205230830.144349-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20221205230830.144349-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2022-12-05 23:08:20 +00:00
|
|
|
return filter;
|
|
|
|
}
|
|
|
|
|
|
|
|
void damos_add_filter(struct damos *s, struct damos_filter *f)
|
|
|
|
{
|
|
|
|
list_add_tail(&f->list, &s->filters);
|
|
|
|
}
|
|
|
|
|
|
|
|
static void damos_del_filter(struct damos_filter *f)
|
|
|
|
{
|
|
|
|
list_del(&f->list);
|
|
|
|
}
|
|
|
|
|
|
|
|
static void damos_free_filter(struct damos_filter *f)
|
|
|
|
{
|
|
|
|
kfree(f);
|
|
|
|
}
|
|
|
|
|
|
|
|
void damos_destroy_filter(struct damos_filter *f)
|
|
|
|
{
|
|
|
|
damos_del_filter(f);
|
|
|
|
damos_free_filter(f);
|
|
|
|
}
|
|
|
|
|
2022-09-13 17:44:31 +00:00
|
|
|
/* initialize private fields of damos_quota and return the pointer */
|
|
|
|
static struct damos_quota *damos_quota_init_priv(struct damos_quota *quota)
|
|
|
|
{
|
|
|
|
quota->total_charged_sz = 0;
|
|
|
|
quota->total_charged_ns = 0;
|
|
|
|
quota->esz = 0;
|
|
|
|
quota->charged_sz = 0;
|
|
|
|
quota->charged_from = 0;
|
|
|
|
quota->charge_target_from = NULL;
|
|
|
|
quota->charge_addr_from = 0;
|
|
|
|
return quota;
|
|
|
|
}
|
|
|
|
|
2022-09-08 19:14:43 +00:00
|
|
|
struct damos *damon_new_scheme(struct damos_access_pattern *pattern,
|
mm/damon/core: implement scheme-specific apply interval
DAMON-based operation schemes are applied for every aggregation interval.
That was mainly because schemes were using nr_accesses, which be complete
to be used for every aggregation interval. However, the schemes are now
using nr_accesses_bp, which is updated for each sampling interval in a way
that reasonable to be used. Therefore, there is no reason to apply
schemes for each aggregation interval.
The unnecessary alignment with aggregation interval was also making some
use cases of DAMOS tricky. Quotas setting under long aggregation interval
is one such example. Suppose the aggregation interval is ten seconds, and
there is a scheme having CPU quota 100ms per 1s. The scheme will actually
uses 100ms per ten seconds, since it cannobe be applied before next
aggregation interval. The feature is working as intended, but the results
might not that intuitive for some users. This could be fixed by updating
the quota to 1s per 10s. But, in the case, the CPU usage of DAMOS could
look like spikes, and would actually make a bad effect to other
CPU-sensitive workloads.
Implement a dedicated timing interval for each DAMON-based operation
scheme, namely apply_interval. The interval will be sampling interval
aligned, and each scheme will be applied for its apply_interval. The
interval is set to 0 by default, and it means the scheme should use the
aggregation interval instead. This avoids old users getting any
behavioral difference.
Link: https://lkml.kernel.org/r/20230916020945.47296-5-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (Google) <rostedt@goodmis.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-16 02:09:40 +00:00
|
|
|
enum damos_action action,
|
|
|
|
unsigned long apply_interval_us,
|
|
|
|
struct damos_quota *quota,
|
2022-09-08 19:14:43 +00:00
|
|
|
struct damos_watermarks *wmarks)
|
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS)
In many cases, users might use DAMON for simple data access aware memory
management optimizations such as applying an operation scheme to a
memory region of a specific size having a specific access frequency for
a specific time. For example, "page out a memory region larger than 100
MiB but having a low access frequency more than 10 minutes", or "Use THP
for a memory region larger than 2 MiB having a high access frequency for
more than 2 seconds".
Most simple form of the solution would be doing offline data access
pattern profiling using DAMON and modifying the application source code
or system configuration based on the profiling results. Or, developing
a daemon constructed with two modules (one for access monitoring and the
other for applying memory management actions via mlock(), madvise(),
sysctl, etc) is imaginable.
To avoid users spending their time for implementation of such simple
data access monitoring-based operation schemes, this makes DAMON to
handle such schemes directly. With this change, users can simply
specify their desired schemes to DAMON. Then, DAMON will automatically
apply the schemes to the user-specified target processes.
Each of the schemes is composed with conditions for filtering of the
target memory regions and desired memory management action for the
target. Specifically, the format is::
<min/max size> <min/max access frequency> <min/max age> <action>
The filtering conditions are size of memory region, number of accesses
to the region monitored by DAMON, and the age of the region. The age of
region is incremented periodically but reset when its addresses or
access frequency has significantly changed or the action of a scheme was
applied. For the action, current implementation supports a few of
madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``,
and ``NOHUGEPAGE``.
Because DAMON supports various address spaces and application of the
actions to a monitoring target region is dependent to the type of the
target address space, the application code should be implemented by each
primitives and registered to the framework. Note that this only
implements the framework part. Following commit will implement the
action applications for virtual address spaces primitives.
Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rienjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:22 +00:00
|
|
|
{
|
|
|
|
struct damos *scheme;
|
|
|
|
|
|
|
|
scheme = kmalloc(sizeof(*scheme), GFP_KERNEL);
|
|
|
|
if (!scheme)
|
|
|
|
return NULL;
|
2022-09-13 17:44:30 +00:00
|
|
|
scheme->pattern = *pattern;
|
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS)
In many cases, users might use DAMON for simple data access aware memory
management optimizations such as applying an operation scheme to a
memory region of a specific size having a specific access frequency for
a specific time. For example, "page out a memory region larger than 100
MiB but having a low access frequency more than 10 minutes", or "Use THP
for a memory region larger than 2 MiB having a high access frequency for
more than 2 seconds".
Most simple form of the solution would be doing offline data access
pattern profiling using DAMON and modifying the application source code
or system configuration based on the profiling results. Or, developing
a daemon constructed with two modules (one for access monitoring and the
other for applying memory management actions via mlock(), madvise(),
sysctl, etc) is imaginable.
To avoid users spending their time for implementation of such simple
data access monitoring-based operation schemes, this makes DAMON to
handle such schemes directly. With this change, users can simply
specify their desired schemes to DAMON. Then, DAMON will automatically
apply the schemes to the user-specified target processes.
Each of the schemes is composed with conditions for filtering of the
target memory regions and desired memory management action for the
target. Specifically, the format is::
<min/max size> <min/max access frequency> <min/max age> <action>
The filtering conditions are size of memory region, number of accesses
to the region monitored by DAMON, and the age of the region. The age of
region is incremented periodically but reset when its addresses or
access frequency has significantly changed or the action of a scheme was
applied. For the action, current implementation supports a few of
madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``,
and ``NOHUGEPAGE``.
Because DAMON supports various address spaces and application of the
actions to a monitoring target region is dependent to the type of the
target address space, the application code should be implemented by each
primitives and registered to the framework. Note that this only
implements the framework part. Following commit will implement the
action applications for virtual address spaces primitives.
Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rienjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:22 +00:00
|
|
|
scheme->action = action;
|
mm/damon/core: implement scheme-specific apply interval
DAMON-based operation schemes are applied for every aggregation interval.
That was mainly because schemes were using nr_accesses, which be complete
to be used for every aggregation interval. However, the schemes are now
using nr_accesses_bp, which is updated for each sampling interval in a way
that reasonable to be used. Therefore, there is no reason to apply
schemes for each aggregation interval.
The unnecessary alignment with aggregation interval was also making some
use cases of DAMOS tricky. Quotas setting under long aggregation interval
is one such example. Suppose the aggregation interval is ten seconds, and
there is a scheme having CPU quota 100ms per 1s. The scheme will actually
uses 100ms per ten seconds, since it cannobe be applied before next
aggregation interval. The feature is working as intended, but the results
might not that intuitive for some users. This could be fixed by updating
the quota to 1s per 10s. But, in the case, the CPU usage of DAMOS could
look like spikes, and would actually make a bad effect to other
CPU-sensitive workloads.
Implement a dedicated timing interval for each DAMON-based operation
scheme, namely apply_interval. The interval will be sampling interval
aligned, and each scheme will be applied for its apply_interval. The
interval is set to 0 by default, and it means the scheme should use the
aggregation interval instead. This avoids old users getting any
behavioral difference.
Link: https://lkml.kernel.org/r/20230916020945.47296-5-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (Google) <rostedt@goodmis.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-16 02:09:40 +00:00
|
|
|
scheme->apply_interval_us = apply_interval_us;
|
|
|
|
/*
|
|
|
|
* next_apply_sis will be set when kdamond starts. While kdamond is
|
|
|
|
* running, it will also updated when it is added to the DAMON context,
|
|
|
|
* or damon_attrs are updated.
|
|
|
|
*/
|
|
|
|
scheme->next_apply_sis = 0;
|
mm/damon/core: implement damos filter
Patch series "implement DAMOS filtering for anon pages and/or specific
memory cgroups"
DAMOS let users do system operations in a data access pattern oriented
way. The data access pattern, which is extracted by DAMON, is somewhat
accurate more than what user space could know in many cases. However, in
some situation, users could know something more than the kernel about the
pattern or some special requirements for some types of memory or
processes. For example, some users would have slow swap devices and knows
latency-ciritical processes and therefore want to use DAMON-based
proactive reclamation (DAMON_RECLAIM) for only non-anonymous pages of
non-latency-critical processes.
For such restriction, users could exclude the memory regions from the
initial monitoring regions and use non-dynamic monitoring regions update
monitoring operations set including fvaddr and paddr. They could also
adjust the DAMOS target access pattern. For dynamically changing memory
layout and access pattern, those would be not enough.
To help the case, add an interface, namely DAMOS filters, which can be
used to avoid the DAMOS actions be applied to specific types of memory, to
DAMON kernel API (damon.h). At the moment, it supports filtering
anonymous pages and/or specific memory cgroups in or out for each DAMOS
scheme.
This patchset adds the support for all DAMOS actions that 'paddr'
monitoring operations set supports ('pageout', 'lru_prio', and
'lru_deprio'), and the functionality is exposed via DAMON kernel API
(damon.h) the DAMON sysfs interface (/sys/kernel/mm/damon/admins/), and
DAMON_RECLAIM module parameters.
Patches Sequence
----------------
First patch implements DAMOS filter interface to DAMON kernel API. Second
patch makes the physical address space monitoring operations set to
support the filters from all supporting DAMOS actions. Third patch adds
anonymous pages filter support to DAMON_RECLAIM, and the fourth patch
documents the DAMON_RECLAIM's new feature. Fifth to seventh patches
implement DAMON sysfs files for support of the filters, and eighth patch
connects the file to use DAMOS filters feature. Ninth patch adds simple
self test cases for DAMOS filters of the sysfs interface. Finally,
following two patches (tenth and eleventh) document the new features and
interfaces.
This patch (of 11):
DAMOS lets users do system operation in a data access pattern oriented
way. The data access pattern, which is extracted by DAMON, is somewhat
accurate more than what user space could know in many cases. However, in
some situation, users could know something more than the kernel about the
pattern or some special requirements for some types of memory or
processes. For example, some users would have slow swap devices and knows
latency-ciritical processes and therefore want to use DAMON-based
proactive reclamation (DAMON_RECLAIM) for only non-anonymous pages of
non-latency-critical processes.
For such restriction, users could exclude the memory regions from the
initial monitoring regions and use non-dynamic monitoring regions update
monitoring operations set including fvaddr and paddr. They could also
adjust the DAMOS target access pattern. For dynamically changing memory
layout and access pattern, those would be not enough.
To help the case, add an interface, namely DAMOS filters, which can be
used to avoid the DAMOS actions be applied to specific types of memory, to
DAMON kernel API (damon.h). At the moment, it supports filtering
anonymous pages and/or specific memory cgroups in or out for each DAMOS
scheme.
Note that this commit adds only the interface to the DAMON kernel API.
The impelmentation should be made in the monitoring operations sets, and
following commits will add that.
Link: https://lkml.kernel.org/r/20221205230830.144349-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20221205230830.144349-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2022-12-05 23:08:20 +00:00
|
|
|
INIT_LIST_HEAD(&scheme->filters);
|
mm/damon/schemes: account scheme actions that successfully applied
Patch series "mm/damon/schemes: Extend stats for better online analysis and tuning".
To help online access pattern analysis and tuning of DAMON-based
Operation Schemes (DAMOS), DAMOS provides simple statistics for each
scheme. Introduction of DAMOS time/space quota further made the tuning
easier by making the risk management easier. However, that also made
understanding of the working schemes a little bit more difficult.
For an example, progress of a given scheme can now be throttled by not
only the aggressiveness of the target access pattern, but also the
time/space quotas. So, when a scheme is showing unexpectedly slow
progress, it's difficult to know by what the progress of the scheme is
throttled, with currently provided statistics.
This patchset extends the statistics to contain some metrics that can be
helpful for such online schemes analysis and tuning (patches 1-2),
exports those to users (patches 3 and 5), and add documents (patches 4
and 6).
This patch (of 6):
DAMON-based operation schemes (DAMOS) stats provide only the number and
the amount of regions that the action of the scheme has tried to be
applied. Because the action could be failed for some reasons, the
currently provided information is sometimes not useful or convenient
enough for schemes profiling and tuning. To improve this situation,
this commit extends the DAMOS stats to provide the number and the amount
of regions that the action has successfully applied.
Link: https://lkml.kernel.org/r/20211210150016.35349-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20211210150016.35349-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-01-14 22:10:17 +00:00
|
|
|
scheme->stat = (struct damos_stat){};
|
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS)
In many cases, users might use DAMON for simple data access aware memory
management optimizations such as applying an operation scheme to a
memory region of a specific size having a specific access frequency for
a specific time. For example, "page out a memory region larger than 100
MiB but having a low access frequency more than 10 minutes", or "Use THP
for a memory region larger than 2 MiB having a high access frequency for
more than 2 seconds".
Most simple form of the solution would be doing offline data access
pattern profiling using DAMON and modifying the application source code
or system configuration based on the profiling results. Or, developing
a daemon constructed with two modules (one for access monitoring and the
other for applying memory management actions via mlock(), madvise(),
sysctl, etc) is imaginable.
To avoid users spending their time for implementation of such simple
data access monitoring-based operation schemes, this makes DAMON to
handle such schemes directly. With this change, users can simply
specify their desired schemes to DAMON. Then, DAMON will automatically
apply the schemes to the user-specified target processes.
Each of the schemes is composed with conditions for filtering of the
target memory regions and desired memory management action for the
target. Specifically, the format is::
<min/max size> <min/max access frequency> <min/max age> <action>
The filtering conditions are size of memory region, number of accesses
to the region monitored by DAMON, and the age of the region. The age of
region is incremented periodically but reset when its addresses or
access frequency has significantly changed or the action of a scheme was
applied. For the action, current implementation supports a few of
madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``,
and ``NOHUGEPAGE``.
Because DAMON supports various address spaces and application of the
actions to a monitoring target region is dependent to the type of the
target address space, the application code should be implemented by each
primitives and registered to the framework. Note that this only
implements the framework part. Following commit will implement the
action applications for virtual address spaces primitives.
Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rienjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:22 +00:00
|
|
|
INIT_LIST_HEAD(&scheme->list);
|
|
|
|
|
2022-09-13 17:44:31 +00:00
|
|
|
scheme->quota = *(damos_quota_init_priv(quota));
|
2021-11-05 20:47:16 +00:00
|
|
|
|
2022-09-13 17:44:30 +00:00
|
|
|
scheme->wmarks = *wmarks;
|
mm/damon/schemes: activate schemes based on a watermarks mechanism
DAMON-based operation schemes need to be manually turned on and off. In
some use cases, however, the condition for turning a scheme on and off
would depend on the system's situation. For example, schemes for
proactive pages reclamation would need to be turned on when some memory
pressure is detected, and turned off when the system has enough free
memory.
For easier control of schemes activation based on the system situation,
this introduces a watermarks-based mechanism. The client can describe
the watermark metric (e.g., amount of free memory in the system),
watermark check interval, and three watermarks, namely high, mid, and
low. If the scheme is deactivated, it only gets the metric and compare
that to the three watermarks for every check interval. If the metric is
higher than the high watermark, the scheme is deactivated. If the
metric is between the mid watermark and the low watermark, the scheme is
activated. If the metric is lower than the low watermark, the scheme is
deactivated again. This is to allow users fall back to traditional
page-granularity mechanisms.
Link: https://lkml.kernel.org/r/20211019150731.16699-12-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:47:47 +00:00
|
|
|
scheme->wmarks.activated = true;
|
|
|
|
|
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS)
In many cases, users might use DAMON for simple data access aware memory
management optimizations such as applying an operation scheme to a
memory region of a specific size having a specific access frequency for
a specific time. For example, "page out a memory region larger than 100
MiB but having a low access frequency more than 10 minutes", or "Use THP
for a memory region larger than 2 MiB having a high access frequency for
more than 2 seconds".
Most simple form of the solution would be doing offline data access
pattern profiling using DAMON and modifying the application source code
or system configuration based on the profiling results. Or, developing
a daemon constructed with two modules (one for access monitoring and the
other for applying memory management actions via mlock(), madvise(),
sysctl, etc) is imaginable.
To avoid users spending their time for implementation of such simple
data access monitoring-based operation schemes, this makes DAMON to
handle such schemes directly. With this change, users can simply
specify their desired schemes to DAMON. Then, DAMON will automatically
apply the schemes to the user-specified target processes.
Each of the schemes is composed with conditions for filtering of the
target memory regions and desired memory management action for the
target. Specifically, the format is::
<min/max size> <min/max access frequency> <min/max age> <action>
The filtering conditions are size of memory region, number of accesses
to the region monitored by DAMON, and the age of the region. The age of
region is incremented periodically but reset when its addresses or
access frequency has significantly changed or the action of a scheme was
applied. For the action, current implementation supports a few of
madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``,
and ``NOHUGEPAGE``.
Because DAMON supports various address spaces and application of the
actions to a monitoring target region is dependent to the type of the
target address space, the application code should be implemented by each
primitives and registered to the framework. Note that this only
implements the framework part. Following commit will implement the
action applications for virtual address spaces primitives.
Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rienjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:22 +00:00
|
|
|
return scheme;
|
|
|
|
}
|
|
|
|
|
mm/damon/core: implement scheme-specific apply interval
DAMON-based operation schemes are applied for every aggregation interval.
That was mainly because schemes were using nr_accesses, which be complete
to be used for every aggregation interval. However, the schemes are now
using nr_accesses_bp, which is updated for each sampling interval in a way
that reasonable to be used. Therefore, there is no reason to apply
schemes for each aggregation interval.
The unnecessary alignment with aggregation interval was also making some
use cases of DAMOS tricky. Quotas setting under long aggregation interval
is one such example. Suppose the aggregation interval is ten seconds, and
there is a scheme having CPU quota 100ms per 1s. The scheme will actually
uses 100ms per ten seconds, since it cannobe be applied before next
aggregation interval. The feature is working as intended, but the results
might not that intuitive for some users. This could be fixed by updating
the quota to 1s per 10s. But, in the case, the CPU usage of DAMOS could
look like spikes, and would actually make a bad effect to other
CPU-sensitive workloads.
Implement a dedicated timing interval for each DAMON-based operation
scheme, namely apply_interval. The interval will be sampling interval
aligned, and each scheme will be applied for its apply_interval. The
interval is set to 0 by default, and it means the scheme should use the
aggregation interval instead. This avoids old users getting any
behavioral difference.
Link: https://lkml.kernel.org/r/20230916020945.47296-5-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (Google) <rostedt@goodmis.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-16 02:09:40 +00:00
|
|
|
static void damos_set_next_apply_sis(struct damos *s, struct damon_ctx *ctx)
|
|
|
|
{
|
|
|
|
unsigned long sample_interval = ctx->attrs.sample_interval ?
|
|
|
|
ctx->attrs.sample_interval : 1;
|
|
|
|
unsigned long apply_interval = s->apply_interval_us ?
|
|
|
|
s->apply_interval_us : ctx->attrs.aggr_interval;
|
|
|
|
|
|
|
|
s->next_apply_sis = ctx->passed_sample_intervals +
|
|
|
|
apply_interval / sample_interval;
|
|
|
|
}
|
|
|
|
|
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS)
In many cases, users might use DAMON for simple data access aware memory
management optimizations such as applying an operation scheme to a
memory region of a specific size having a specific access frequency for
a specific time. For example, "page out a memory region larger than 100
MiB but having a low access frequency more than 10 minutes", or "Use THP
for a memory region larger than 2 MiB having a high access frequency for
more than 2 seconds".
Most simple form of the solution would be doing offline data access
pattern profiling using DAMON and modifying the application source code
or system configuration based on the profiling results. Or, developing
a daemon constructed with two modules (one for access monitoring and the
other for applying memory management actions via mlock(), madvise(),
sysctl, etc) is imaginable.
To avoid users spending their time for implementation of such simple
data access monitoring-based operation schemes, this makes DAMON to
handle such schemes directly. With this change, users can simply
specify their desired schemes to DAMON. Then, DAMON will automatically
apply the schemes to the user-specified target processes.
Each of the schemes is composed with conditions for filtering of the
target memory regions and desired memory management action for the
target. Specifically, the format is::
<min/max size> <min/max access frequency> <min/max age> <action>
The filtering conditions are size of memory region, number of accesses
to the region monitored by DAMON, and the age of the region. The age of
region is incremented periodically but reset when its addresses or
access frequency has significantly changed or the action of a scheme was
applied. For the action, current implementation supports a few of
madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``,
and ``NOHUGEPAGE``.
Because DAMON supports various address spaces and application of the
actions to a monitoring target region is dependent to the type of the
target address space, the application code should be implemented by each
primitives and registered to the framework. Note that this only
implements the framework part. Following commit will implement the
action applications for virtual address spaces primitives.
Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rienjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:22 +00:00
|
|
|
void damon_add_scheme(struct damon_ctx *ctx, struct damos *s)
|
|
|
|
{
|
|
|
|
list_add_tail(&s->list, &ctx->schemes);
|
mm/damon/core: implement scheme-specific apply interval
DAMON-based operation schemes are applied for every aggregation interval.
That was mainly because schemes were using nr_accesses, which be complete
to be used for every aggregation interval. However, the schemes are now
using nr_accesses_bp, which is updated for each sampling interval in a way
that reasonable to be used. Therefore, there is no reason to apply
schemes for each aggregation interval.
The unnecessary alignment with aggregation interval was also making some
use cases of DAMOS tricky. Quotas setting under long aggregation interval
is one such example. Suppose the aggregation interval is ten seconds, and
there is a scheme having CPU quota 100ms per 1s. The scheme will actually
uses 100ms per ten seconds, since it cannobe be applied before next
aggregation interval. The feature is working as intended, but the results
might not that intuitive for some users. This could be fixed by updating
the quota to 1s per 10s. But, in the case, the CPU usage of DAMOS could
look like spikes, and would actually make a bad effect to other
CPU-sensitive workloads.
Implement a dedicated timing interval for each DAMON-based operation
scheme, namely apply_interval. The interval will be sampling interval
aligned, and each scheme will be applied for its apply_interval. The
interval is set to 0 by default, and it means the scheme should use the
aggregation interval instead. This avoids old users getting any
behavioral difference.
Link: https://lkml.kernel.org/r/20230916020945.47296-5-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (Google) <rostedt@goodmis.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-16 02:09:40 +00:00
|
|
|
damos_set_next_apply_sis(s, ctx);
|
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS)
In many cases, users might use DAMON for simple data access aware memory
management optimizations such as applying an operation scheme to a
memory region of a specific size having a specific access frequency for
a specific time. For example, "page out a memory region larger than 100
MiB but having a low access frequency more than 10 minutes", or "Use THP
for a memory region larger than 2 MiB having a high access frequency for
more than 2 seconds".
Most simple form of the solution would be doing offline data access
pattern profiling using DAMON and modifying the application source code
or system configuration based on the profiling results. Or, developing
a daemon constructed with two modules (one for access monitoring and the
other for applying memory management actions via mlock(), madvise(),
sysctl, etc) is imaginable.
To avoid users spending their time for implementation of such simple
data access monitoring-based operation schemes, this makes DAMON to
handle such schemes directly. With this change, users can simply
specify their desired schemes to DAMON. Then, DAMON will automatically
apply the schemes to the user-specified target processes.
Each of the schemes is composed with conditions for filtering of the
target memory regions and desired memory management action for the
target. Specifically, the format is::
<min/max size> <min/max access frequency> <min/max age> <action>
The filtering conditions are size of memory region, number of accesses
to the region monitored by DAMON, and the age of the region. The age of
region is incremented periodically but reset when its addresses or
access frequency has significantly changed or the action of a scheme was
applied. For the action, current implementation supports a few of
madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``,
and ``NOHUGEPAGE``.
Because DAMON supports various address spaces and application of the
actions to a monitoring target region is dependent to the type of the
target address space, the application code should be implemented by each
primitives and registered to the framework. Note that this only
implements the framework part. Following commit will implement the
action applications for virtual address spaces primitives.
Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rienjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:22 +00:00
|
|
|
}
|
|
|
|
|
|
|
|
static void damon_del_scheme(struct damos *s)
|
|
|
|
{
|
|
|
|
list_del(&s->list);
|
|
|
|
}
|
|
|
|
|
|
|
|
static void damon_free_scheme(struct damos *s)
|
|
|
|
{
|
|
|
|
kfree(s);
|
|
|
|
}
|
|
|
|
|
|
|
|
void damon_destroy_scheme(struct damos *s)
|
|
|
|
{
|
mm/damon/core: implement damos filter
Patch series "implement DAMOS filtering for anon pages and/or specific
memory cgroups"
DAMOS let users do system operations in a data access pattern oriented
way. The data access pattern, which is extracted by DAMON, is somewhat
accurate more than what user space could know in many cases. However, in
some situation, users could know something more than the kernel about the
pattern or some special requirements for some types of memory or
processes. For example, some users would have slow swap devices and knows
latency-ciritical processes and therefore want to use DAMON-based
proactive reclamation (DAMON_RECLAIM) for only non-anonymous pages of
non-latency-critical processes.
For such restriction, users could exclude the memory regions from the
initial monitoring regions and use non-dynamic monitoring regions update
monitoring operations set including fvaddr and paddr. They could also
adjust the DAMOS target access pattern. For dynamically changing memory
layout and access pattern, those would be not enough.
To help the case, add an interface, namely DAMOS filters, which can be
used to avoid the DAMOS actions be applied to specific types of memory, to
DAMON kernel API (damon.h). At the moment, it supports filtering
anonymous pages and/or specific memory cgroups in or out for each DAMOS
scheme.
This patchset adds the support for all DAMOS actions that 'paddr'
monitoring operations set supports ('pageout', 'lru_prio', and
'lru_deprio'), and the functionality is exposed via DAMON kernel API
(damon.h) the DAMON sysfs interface (/sys/kernel/mm/damon/admins/), and
DAMON_RECLAIM module parameters.
Patches Sequence
----------------
First patch implements DAMOS filter interface to DAMON kernel API. Second
patch makes the physical address space monitoring operations set to
support the filters from all supporting DAMOS actions. Third patch adds
anonymous pages filter support to DAMON_RECLAIM, and the fourth patch
documents the DAMON_RECLAIM's new feature. Fifth to seventh patches
implement DAMON sysfs files for support of the filters, and eighth patch
connects the file to use DAMOS filters feature. Ninth patch adds simple
self test cases for DAMOS filters of the sysfs interface. Finally,
following two patches (tenth and eleventh) document the new features and
interfaces.
This patch (of 11):
DAMOS lets users do system operation in a data access pattern oriented
way. The data access pattern, which is extracted by DAMON, is somewhat
accurate more than what user space could know in many cases. However, in
some situation, users could know something more than the kernel about the
pattern or some special requirements for some types of memory or
processes. For example, some users would have slow swap devices and knows
latency-ciritical processes and therefore want to use DAMON-based
proactive reclamation (DAMON_RECLAIM) for only non-anonymous pages of
non-latency-critical processes.
For such restriction, users could exclude the memory regions from the
initial monitoring regions and use non-dynamic monitoring regions update
monitoring operations set including fvaddr and paddr. They could also
adjust the DAMOS target access pattern. For dynamically changing memory
layout and access pattern, those would be not enough.
To help the case, add an interface, namely DAMOS filters, which can be
used to avoid the DAMOS actions be applied to specific types of memory, to
DAMON kernel API (damon.h). At the moment, it supports filtering
anonymous pages and/or specific memory cgroups in or out for each DAMOS
scheme.
Note that this commit adds only the interface to the DAMON kernel API.
The impelmentation should be made in the monitoring operations sets, and
following commits will add that.
Link: https://lkml.kernel.org/r/20221205230830.144349-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20221205230830.144349-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2022-12-05 23:08:20 +00:00
|
|
|
struct damos_filter *f, *next;
|
|
|
|
|
|
|
|
damos_for_each_filter_safe(f, next, s)
|
|
|
|
damos_destroy_filter(f);
|
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS)
In many cases, users might use DAMON for simple data access aware memory
management optimizations such as applying an operation scheme to a
memory region of a specific size having a specific access frequency for
a specific time. For example, "page out a memory region larger than 100
MiB but having a low access frequency more than 10 minutes", or "Use THP
for a memory region larger than 2 MiB having a high access frequency for
more than 2 seconds".
Most simple form of the solution would be doing offline data access
pattern profiling using DAMON and modifying the application source code
or system configuration based on the profiling results. Or, developing
a daemon constructed with two modules (one for access monitoring and the
other for applying memory management actions via mlock(), madvise(),
sysctl, etc) is imaginable.
To avoid users spending their time for implementation of such simple
data access monitoring-based operation schemes, this makes DAMON to
handle such schemes directly. With this change, users can simply
specify their desired schemes to DAMON. Then, DAMON will automatically
apply the schemes to the user-specified target processes.
Each of the schemes is composed with conditions for filtering of the
target memory regions and desired memory management action for the
target. Specifically, the format is::
<min/max size> <min/max access frequency> <min/max age> <action>
The filtering conditions are size of memory region, number of accesses
to the region monitored by DAMON, and the age of the region. The age of
region is incremented periodically but reset when its addresses or
access frequency has significantly changed or the action of a scheme was
applied. For the action, current implementation supports a few of
madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``,
and ``NOHUGEPAGE``.
Because DAMON supports various address spaces and application of the
actions to a monitoring target region is dependent to the type of the
target address space, the application code should be implemented by each
primitives and registered to the framework. Note that this only
implements the framework part. Following commit will implement the
action applications for virtual address spaces primitives.
Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rienjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:22 +00:00
|
|
|
damon_del_scheme(s);
|
|
|
|
damon_free_scheme(s);
|
|
|
|
}
|
|
|
|
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
/*
|
|
|
|
* Construct a damon_target struct
|
|
|
|
*
|
|
|
|
* Returns the pointer to the new struct if success, or NULL otherwise
|
|
|
|
*/
|
mm/damon: remove the target id concept
DAMON asks each monitoring target ('struct damon_target') to have one
'unsigned long' integer called 'id', which should be unique among the
targets of same monitoring context. Meaning of it is, however, totally up
to the monitoring primitives that registered to the monitoring context.
For example, the virtual address spaces monitoring primitives treats the
id as a 'struct pid' pointer.
This makes the code flexible, but ugly, not well-documented, and
type-unsafe[1]. Also, identification of each target can be done via its
index. For the reason, this commit removes the concept and uses clear
type definition. For now, only 'struct pid' pointer is used for the
virtual address spaces monitoring. If DAMON is extended in future so that
we need to put another identifier field in the struct, we will use a union
for such primitives-dependent fields and document which primitives are
using which type.
[1] https://lore.kernel.org/linux-mm/20211013154535.4aaeaaf9d0182922e405dd1e@linux-foundation.org/
Link: https://lkml.kernel.org/r/20211230100723.2238-5-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-22 21:48:40 +00:00
|
|
|
struct damon_target *damon_new_target(void)
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
{
|
|
|
|
struct damon_target *t;
|
|
|
|
|
|
|
|
t = kmalloc(sizeof(*t), GFP_KERNEL);
|
|
|
|
if (!t)
|
|
|
|
return NULL;
|
|
|
|
|
mm/damon: remove the target id concept
DAMON asks each monitoring target ('struct damon_target') to have one
'unsigned long' integer called 'id', which should be unique among the
targets of same monitoring context. Meaning of it is, however, totally up
to the monitoring primitives that registered to the monitoring context.
For example, the virtual address spaces monitoring primitives treats the
id as a 'struct pid' pointer.
This makes the code flexible, but ugly, not well-documented, and
type-unsafe[1]. Also, identification of each target can be done via its
index. For the reason, this commit removes the concept and uses clear
type definition. For now, only 'struct pid' pointer is used for the
virtual address spaces monitoring. If DAMON is extended in future so that
we need to put another identifier field in the struct, we will use a union
for such primitives-dependent fields and document which primitives are
using which type.
[1] https://lore.kernel.org/linux-mm/20211013154535.4aaeaaf9d0182922e405dd1e@linux-foundation.org/
Link: https://lkml.kernel.org/r/20211230100723.2238-5-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-22 21:48:40 +00:00
|
|
|
t->pid = NULL;
|
2021-09-08 02:56:36 +00:00
|
|
|
t->nr_regions = 0;
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
INIT_LIST_HEAD(&t->regions_list);
|
2022-10-02 19:31:30 +00:00
|
|
|
INIT_LIST_HEAD(&t->list);
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
|
|
|
|
return t;
|
|
|
|
}
|
|
|
|
|
|
|
|
void damon_add_target(struct damon_ctx *ctx, struct damon_target *t)
|
|
|
|
{
|
2021-09-08 02:56:36 +00:00
|
|
|
list_add_tail(&t->list, &ctx->adaptive_targets);
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
}
|
|
|
|
|
2021-11-05 20:48:07 +00:00
|
|
|
bool damon_targets_empty(struct damon_ctx *ctx)
|
|
|
|
{
|
|
|
|
return list_empty(&ctx->adaptive_targets);
|
|
|
|
}
|
|
|
|
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
static void damon_del_target(struct damon_target *t)
|
|
|
|
{
|
|
|
|
list_del(&t->list);
|
|
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|
}
|
|
|
|
|
|
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void damon_free_target(struct damon_target *t)
|
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|
{
|
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|
struct damon_region *r, *next;
|
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|
damon_for_each_region_safe(r, next, t)
|
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|
damon_free_region(r);
|
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kfree(t);
|
|
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|
}
|
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|
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|
|
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void damon_destroy_target(struct damon_target *t)
|
|
|
|
{
|
|
|
|
damon_del_target(t);
|
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|
damon_free_target(t);
|
|
|
|
}
|
|
|
|
|
2021-09-08 02:56:36 +00:00
|
|
|
unsigned int damon_nr_regions(struct damon_target *t)
|
|
|
|
{
|
|
|
|
return t->nr_regions;
|
|
|
|
}
|
|
|
|
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
struct damon_ctx *damon_new_ctx(void)
|
|
|
|
{
|
|
|
|
struct damon_ctx *ctx;
|
|
|
|
|
|
|
|
ctx = kzalloc(sizeof(*ctx), GFP_KERNEL);
|
|
|
|
if (!ctx)
|
|
|
|
return NULL;
|
|
|
|
|
2022-09-13 17:44:32 +00:00
|
|
|
ctx->attrs.sample_interval = 5 * 1000;
|
|
|
|
ctx->attrs.aggr_interval = 100 * 1000;
|
|
|
|
ctx->attrs.ops_update_interval = 60 * 1000 * 1000;
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
|
mm/damon/core: use number of passed access sampling as a timer
DAMON sleeps for sampling interval after each sampling, and check if the
aggregation interval and the ops update interval have passed using
ktime_get_coarse_ts64() and baseline timestamps for the intervals. That
design is for making the operations occur at deterministic timing
regardless of the time that spend for each work. However, it turned out
it is not that useful, and incur not-that-intuitive results.
After all, timer functions, and especially sleep functions that DAMON uses
to wait for specific timing, are not necessarily strictly accurate. It is
legal design, so no problem. However, depending on such inaccuracies, the
nr_accesses can be larger than aggregation interval divided by sampling
interval. For example, with the default setting (5 ms sampling interval
and 100 ms aggregation interval) we frequently show regions having
nr_accesses larger than 20. Also, if the execution of a DAMOS scheme
takes a long time, next aggregation could happen before enough number of
samples are collected. This is not what usual users would intuitively
expect.
Since access check sampling is the smallest unit work of DAMON, using the
number of passed sampling intervals as the DAMON-internal timer can easily
avoid these problems. That is, convert aggregation and ops update
intervals to numbers of sampling intervals that need to be passed before
those operations be executed, count the number of passed sampling
intervals, and invoke the operations as soon as the specific amount of
sampling intervals passed. Make the change.
Note that this could make a behavioral change to settings that using
intervals that not aligned by the sampling interval. For example, if the
sampling interval is 5 ms and the aggregation interval is 12 ms, DAMON
effectively uses 15 ms as its aggregation interval, because it checks
whether the aggregation interval after sleeping the sampling interval.
This change will make DAMON to effectively use 10 ms as aggregation
interval, since it uses 'aggregation interval / sampling interval *
sampling interval' as the effective aggregation interval, and we don't use
floating point types. Usual users would have used aligned intervals, so
this behavioral change is not expected to make any meaningful impact, so
just make this change.
Link: https://lkml.kernel.org/r/20230914021523.60649-1-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-14 02:15:23 +00:00
|
|
|
ctx->passed_sample_intervals = 0;
|
|
|
|
/* These will be set from kdamond_init_intervals_sis() */
|
|
|
|
ctx->next_aggregation_sis = 0;
|
|
|
|
ctx->next_ops_update_sis = 0;
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
|
|
|
|
mutex_init(&ctx->kdamond_lock);
|
|
|
|
|
2022-09-13 17:44:32 +00:00
|
|
|
ctx->attrs.min_nr_regions = 10;
|
|
|
|
ctx->attrs.max_nr_regions = 1000;
|
2021-09-08 02:56:36 +00:00
|
|
|
|
|
|
|
INIT_LIST_HEAD(&ctx->adaptive_targets);
|
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS)
In many cases, users might use DAMON for simple data access aware memory
management optimizations such as applying an operation scheme to a
memory region of a specific size having a specific access frequency for
a specific time. For example, "page out a memory region larger than 100
MiB but having a low access frequency more than 10 minutes", or "Use THP
for a memory region larger than 2 MiB having a high access frequency for
more than 2 seconds".
Most simple form of the solution would be doing offline data access
pattern profiling using DAMON and modifying the application source code
or system configuration based on the profiling results. Or, developing
a daemon constructed with two modules (one for access monitoring and the
other for applying memory management actions via mlock(), madvise(),
sysctl, etc) is imaginable.
To avoid users spending their time for implementation of such simple
data access monitoring-based operation schemes, this makes DAMON to
handle such schemes directly. With this change, users can simply
specify their desired schemes to DAMON. Then, DAMON will automatically
apply the schemes to the user-specified target processes.
Each of the schemes is composed with conditions for filtering of the
target memory regions and desired memory management action for the
target. Specifically, the format is::
<min/max size> <min/max access frequency> <min/max age> <action>
The filtering conditions are size of memory region, number of accesses
to the region monitored by DAMON, and the age of the region. The age of
region is incremented periodically but reset when its addresses or
access frequency has significantly changed or the action of a scheme was
applied. For the action, current implementation supports a few of
madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``,
and ``NOHUGEPAGE``.
Because DAMON supports various address spaces and application of the
actions to a monitoring target region is dependent to the type of the
target address space, the application code should be implemented by each
primitives and registered to the framework. Note that this only
implements the framework part. Following commit will implement the
action applications for virtual address spaces primitives.
Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rienjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:22 +00:00
|
|
|
INIT_LIST_HEAD(&ctx->schemes);
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
|
|
|
|
return ctx;
|
|
|
|
}
|
|
|
|
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
static void damon_destroy_targets(struct damon_ctx *ctx)
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
{
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
struct damon_target *t, *next_t;
|
|
|
|
|
2022-03-22 21:48:46 +00:00
|
|
|
if (ctx->ops.cleanup) {
|
|
|
|
ctx->ops.cleanup(ctx);
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
return;
|
|
|
|
}
|
|
|
|
|
|
|
|
damon_for_each_target_safe(t, next_t, ctx)
|
|
|
|
damon_destroy_target(t);
|
|
|
|
}
|
|
|
|
|
|
|
|
void damon_destroy_ctx(struct damon_ctx *ctx)
|
|
|
|
{
|
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS)
In many cases, users might use DAMON for simple data access aware memory
management optimizations such as applying an operation scheme to a
memory region of a specific size having a specific access frequency for
a specific time. For example, "page out a memory region larger than 100
MiB but having a low access frequency more than 10 minutes", or "Use THP
for a memory region larger than 2 MiB having a high access frequency for
more than 2 seconds".
Most simple form of the solution would be doing offline data access
pattern profiling using DAMON and modifying the application source code
or system configuration based on the profiling results. Or, developing
a daemon constructed with two modules (one for access monitoring and the
other for applying memory management actions via mlock(), madvise(),
sysctl, etc) is imaginable.
To avoid users spending their time for implementation of such simple
data access monitoring-based operation schemes, this makes DAMON to
handle such schemes directly. With this change, users can simply
specify their desired schemes to DAMON. Then, DAMON will automatically
apply the schemes to the user-specified target processes.
Each of the schemes is composed with conditions for filtering of the
target memory regions and desired memory management action for the
target. Specifically, the format is::
<min/max size> <min/max access frequency> <min/max age> <action>
The filtering conditions are size of memory region, number of accesses
to the region monitored by DAMON, and the age of the region. The age of
region is incremented periodically but reset when its addresses or
access frequency has significantly changed or the action of a scheme was
applied. For the action, current implementation supports a few of
madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``,
and ``NOHUGEPAGE``.
Because DAMON supports various address spaces and application of the
actions to a monitoring target region is dependent to the type of the
target address space, the application code should be implemented by each
primitives and registered to the framework. Note that this only
implements the framework part. Following commit will implement the
action applications for virtual address spaces primitives.
Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rienjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:22 +00:00
|
|
|
struct damos *s, *next_s;
|
|
|
|
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
damon_destroy_targets(ctx);
|
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS)
In many cases, users might use DAMON for simple data access aware memory
management optimizations such as applying an operation scheme to a
memory region of a specific size having a specific access frequency for
a specific time. For example, "page out a memory region larger than 100
MiB but having a low access frequency more than 10 minutes", or "Use THP
for a memory region larger than 2 MiB having a high access frequency for
more than 2 seconds".
Most simple form of the solution would be doing offline data access
pattern profiling using DAMON and modifying the application source code
or system configuration based on the profiling results. Or, developing
a daemon constructed with two modules (one for access monitoring and the
other for applying memory management actions via mlock(), madvise(),
sysctl, etc) is imaginable.
To avoid users spending their time for implementation of such simple
data access monitoring-based operation schemes, this makes DAMON to
handle such schemes directly. With this change, users can simply
specify their desired schemes to DAMON. Then, DAMON will automatically
apply the schemes to the user-specified target processes.
Each of the schemes is composed with conditions for filtering of the
target memory regions and desired memory management action for the
target. Specifically, the format is::
<min/max size> <min/max access frequency> <min/max age> <action>
The filtering conditions are size of memory region, number of accesses
to the region monitored by DAMON, and the age of the region. The age of
region is incremented periodically but reset when its addresses or
access frequency has significantly changed or the action of a scheme was
applied. For the action, current implementation supports a few of
madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``,
and ``NOHUGEPAGE``.
Because DAMON supports various address spaces and application of the
actions to a monitoring target region is dependent to the type of the
target address space, the application code should be implemented by each
primitives and registered to the framework. Note that this only
implements the framework part. Following commit will implement the
action applications for virtual address spaces primitives.
Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rienjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:22 +00:00
|
|
|
|
|
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|
damon_for_each_scheme_safe(s, next_s, ctx)
|
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damon_destroy_scheme(s);
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|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
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kfree(ctx);
|
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|
|
}
|
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|
|
2023-01-19 01:38:30 +00:00
|
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static unsigned int damon_age_for_new_attrs(unsigned int age,
|
|
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|
struct damon_attrs *old_attrs, struct damon_attrs *new_attrs)
|
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|
|
{
|
|
|
|
return age * old_attrs->aggr_interval / new_attrs->aggr_interval;
|
|
|
|
}
|
|
|
|
|
|
|
|
/* convert access ratio in bp (per 10,000) to nr_accesses */
|
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|
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static unsigned int damon_accesses_bp_to_nr_accesses(
|
|
|
|
unsigned int accesses_bp, struct damon_attrs *attrs)
|
|
|
|
{
|
2023-10-19 19:49:21 +00:00
|
|
|
return accesses_bp * damon_max_nr_accesses(attrs) / 10000;
|
2023-01-19 01:38:30 +00:00
|
|
|
}
|
|
|
|
|
|
|
|
/* convert nr_accesses to access ratio in bp (per 10,000) */
|
|
|
|
static unsigned int damon_nr_accesses_to_accesses_bp(
|
|
|
|
unsigned int nr_accesses, struct damon_attrs *attrs)
|
|
|
|
{
|
2023-10-19 19:49:21 +00:00
|
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return nr_accesses * 10000 / damon_max_nr_accesses(attrs);
|
2023-01-19 01:38:30 +00:00
|
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}
|
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|
|
|
|
|
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static unsigned int damon_nr_accesses_for_new_attrs(unsigned int nr_accesses,
|
|
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struct damon_attrs *old_attrs, struct damon_attrs *new_attrs)
|
|
|
|
{
|
|
|
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return damon_accesses_bp_to_nr_accesses(
|
|
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|
damon_nr_accesses_to_accesses_bp(
|
|
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|
nr_accesses, old_attrs),
|
|
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|
new_attrs);
|
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|
}
|
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|
|
|
|
|
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static void damon_update_monitoring_result(struct damon_region *r,
|
|
|
|
struct damon_attrs *old_attrs, struct damon_attrs *new_attrs)
|
|
|
|
{
|
|
|
|
r->nr_accesses = damon_nr_accesses_for_new_attrs(r->nr_accesses,
|
|
|
|
old_attrs, new_attrs);
|
2023-09-15 02:52:48 +00:00
|
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|
r->nr_accesses_bp = r->nr_accesses * 10000;
|
2023-01-19 01:38:30 +00:00
|
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|
r->age = damon_age_for_new_attrs(r->age, old_attrs, new_attrs);
|
|
|
|
}
|
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|
|
|
|
|
|
/*
|
|
|
|
* region->nr_accesses is the number of sampling intervals in the last
|
|
|
|
* aggregation interval that access to the region has found, and region->age is
|
|
|
|
* the number of aggregation intervals that its access pattern has maintained.
|
|
|
|
* For the reason, the real meaning of the two fields depend on current
|
|
|
|
* sampling interval and aggregation interval. This function updates
|
|
|
|
* ->nr_accesses and ->age of given damon_ctx's regions for new damon_attrs.
|
|
|
|
*/
|
|
|
|
static void damon_update_monitoring_results(struct damon_ctx *ctx,
|
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|
|
struct damon_attrs *new_attrs)
|
|
|
|
{
|
|
|
|
struct damon_attrs *old_attrs = &ctx->attrs;
|
|
|
|
struct damon_target *t;
|
|
|
|
struct damon_region *r;
|
|
|
|
|
|
|
|
/* if any interval is zero, simply forgive conversion */
|
|
|
|
if (!old_attrs->sample_interval || !old_attrs->aggr_interval ||
|
|
|
|
!new_attrs->sample_interval ||
|
|
|
|
!new_attrs->aggr_interval)
|
|
|
|
return;
|
|
|
|
|
|
|
|
damon_for_each_target(t, ctx)
|
|
|
|
damon_for_each_region(r, t)
|
|
|
|
damon_update_monitoring_result(
|
|
|
|
r, old_attrs, new_attrs);
|
|
|
|
}
|
|
|
|
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
/**
|
|
|
|
* damon_set_attrs() - Set attributes for the monitoring.
|
|
|
|
* @ctx: monitoring context
|
2022-09-13 17:44:33 +00:00
|
|
|
* @attrs: monitoring attributes
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
*
|
2023-09-07 02:29:26 +00:00
|
|
|
* This function should be called while the kdamond is not running, or an
|
|
|
|
* access check results aggregation is not ongoing (e.g., from
|
|
|
|
* &struct damon_callback->after_aggregation or
|
|
|
|
* &struct damon_callback->after_wmarks_check callbacks).
|
|
|
|
*
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
* Every time interval is in micro-seconds.
|
|
|
|
*
|
|
|
|
* Return: 0 on success, negative error code otherwise.
|
|
|
|
*/
|
2022-09-13 17:44:33 +00:00
|
|
|
int damon_set_attrs(struct damon_ctx *ctx, struct damon_attrs *attrs)
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
{
|
mm/damon/core: use number of passed access sampling as a timer
DAMON sleeps for sampling interval after each sampling, and check if the
aggregation interval and the ops update interval have passed using
ktime_get_coarse_ts64() and baseline timestamps for the intervals. That
design is for making the operations occur at deterministic timing
regardless of the time that spend for each work. However, it turned out
it is not that useful, and incur not-that-intuitive results.
After all, timer functions, and especially sleep functions that DAMON uses
to wait for specific timing, are not necessarily strictly accurate. It is
legal design, so no problem. However, depending on such inaccuracies, the
nr_accesses can be larger than aggregation interval divided by sampling
interval. For example, with the default setting (5 ms sampling interval
and 100 ms aggregation interval) we frequently show regions having
nr_accesses larger than 20. Also, if the execution of a DAMOS scheme
takes a long time, next aggregation could happen before enough number of
samples are collected. This is not what usual users would intuitively
expect.
Since access check sampling is the smallest unit work of DAMON, using the
number of passed sampling intervals as the DAMON-internal timer can easily
avoid these problems. That is, convert aggregation and ops update
intervals to numbers of sampling intervals that need to be passed before
those operations be executed, count the number of passed sampling
intervals, and invoke the operations as soon as the specific amount of
sampling intervals passed. Make the change.
Note that this could make a behavioral change to settings that using
intervals that not aligned by the sampling interval. For example, if the
sampling interval is 5 ms and the aggregation interval is 12 ms, DAMON
effectively uses 15 ms as its aggregation interval, because it checks
whether the aggregation interval after sleeping the sampling interval.
This change will make DAMON to effectively use 10 ms as aggregation
interval, since it uses 'aggregation interval / sampling interval *
sampling interval' as the effective aggregation interval, and we don't use
floating point types. Usual users would have used aligned intervals, so
this behavioral change is not expected to make any meaningful impact, so
just make this change.
Link: https://lkml.kernel.org/r/20230914021523.60649-1-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-14 02:15:23 +00:00
|
|
|
unsigned long sample_interval = attrs->sample_interval ?
|
|
|
|
attrs->sample_interval : 1;
|
mm/damon/core: implement scheme-specific apply interval
DAMON-based operation schemes are applied for every aggregation interval.
That was mainly because schemes were using nr_accesses, which be complete
to be used for every aggregation interval. However, the schemes are now
using nr_accesses_bp, which is updated for each sampling interval in a way
that reasonable to be used. Therefore, there is no reason to apply
schemes for each aggregation interval.
The unnecessary alignment with aggregation interval was also making some
use cases of DAMOS tricky. Quotas setting under long aggregation interval
is one such example. Suppose the aggregation interval is ten seconds, and
there is a scheme having CPU quota 100ms per 1s. The scheme will actually
uses 100ms per ten seconds, since it cannobe be applied before next
aggregation interval. The feature is working as intended, but the results
might not that intuitive for some users. This could be fixed by updating
the quota to 1s per 10s. But, in the case, the CPU usage of DAMOS could
look like spikes, and would actually make a bad effect to other
CPU-sensitive workloads.
Implement a dedicated timing interval for each DAMON-based operation
scheme, namely apply_interval. The interval will be sampling interval
aligned, and each scheme will be applied for its apply_interval. The
interval is set to 0 by default, and it means the scheme should use the
aggregation interval instead. This avoids old users getting any
behavioral difference.
Link: https://lkml.kernel.org/r/20230916020945.47296-5-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (Google) <rostedt@goodmis.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-16 02:09:40 +00:00
|
|
|
struct damos *s;
|
mm/damon/core: use number of passed access sampling as a timer
DAMON sleeps for sampling interval after each sampling, and check if the
aggregation interval and the ops update interval have passed using
ktime_get_coarse_ts64() and baseline timestamps for the intervals. That
design is for making the operations occur at deterministic timing
regardless of the time that spend for each work. However, it turned out
it is not that useful, and incur not-that-intuitive results.
After all, timer functions, and especially sleep functions that DAMON uses
to wait for specific timing, are not necessarily strictly accurate. It is
legal design, so no problem. However, depending on such inaccuracies, the
nr_accesses can be larger than aggregation interval divided by sampling
interval. For example, with the default setting (5 ms sampling interval
and 100 ms aggregation interval) we frequently show regions having
nr_accesses larger than 20. Also, if the execution of a DAMOS scheme
takes a long time, next aggregation could happen before enough number of
samples are collected. This is not what usual users would intuitively
expect.
Since access check sampling is the smallest unit work of DAMON, using the
number of passed sampling intervals as the DAMON-internal timer can easily
avoid these problems. That is, convert aggregation and ops update
intervals to numbers of sampling intervals that need to be passed before
those operations be executed, count the number of passed sampling
intervals, and invoke the operations as soon as the specific amount of
sampling intervals passed. Make the change.
Note that this could make a behavioral change to settings that using
intervals that not aligned by the sampling interval. For example, if the
sampling interval is 5 ms and the aggregation interval is 12 ms, DAMON
effectively uses 15 ms as its aggregation interval, because it checks
whether the aggregation interval after sleeping the sampling interval.
This change will make DAMON to effectively use 10 ms as aggregation
interval, since it uses 'aggregation interval / sampling interval *
sampling interval' as the effective aggregation interval, and we don't use
floating point types. Usual users would have used aligned intervals, so
this behavioral change is not expected to make any meaningful impact, so
just make this change.
Link: https://lkml.kernel.org/r/20230914021523.60649-1-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-14 02:15:23 +00:00
|
|
|
|
2022-09-13 17:44:33 +00:00
|
|
|
if (attrs->min_nr_regions < 3)
|
2021-09-08 02:56:36 +00:00
|
|
|
return -EINVAL;
|
2022-09-13 17:44:33 +00:00
|
|
|
if (attrs->min_nr_regions > attrs->max_nr_regions)
|
2021-09-08 02:56:36 +00:00
|
|
|
return -EINVAL;
|
2023-05-27 03:21:01 +00:00
|
|
|
if (attrs->sample_interval > attrs->aggr_interval)
|
|
|
|
return -EINVAL;
|
2021-09-08 02:56:36 +00:00
|
|
|
|
mm/damon/core: use number of passed access sampling as a timer
DAMON sleeps for sampling interval after each sampling, and check if the
aggregation interval and the ops update interval have passed using
ktime_get_coarse_ts64() and baseline timestamps for the intervals. That
design is for making the operations occur at deterministic timing
regardless of the time that spend for each work. However, it turned out
it is not that useful, and incur not-that-intuitive results.
After all, timer functions, and especially sleep functions that DAMON uses
to wait for specific timing, are not necessarily strictly accurate. It is
legal design, so no problem. However, depending on such inaccuracies, the
nr_accesses can be larger than aggregation interval divided by sampling
interval. For example, with the default setting (5 ms sampling interval
and 100 ms aggregation interval) we frequently show regions having
nr_accesses larger than 20. Also, if the execution of a DAMOS scheme
takes a long time, next aggregation could happen before enough number of
samples are collected. This is not what usual users would intuitively
expect.
Since access check sampling is the smallest unit work of DAMON, using the
number of passed sampling intervals as the DAMON-internal timer can easily
avoid these problems. That is, convert aggregation and ops update
intervals to numbers of sampling intervals that need to be passed before
those operations be executed, count the number of passed sampling
intervals, and invoke the operations as soon as the specific amount of
sampling intervals passed. Make the change.
Note that this could make a behavioral change to settings that using
intervals that not aligned by the sampling interval. For example, if the
sampling interval is 5 ms and the aggregation interval is 12 ms, DAMON
effectively uses 15 ms as its aggregation interval, because it checks
whether the aggregation interval after sleeping the sampling interval.
This change will make DAMON to effectively use 10 ms as aggregation
interval, since it uses 'aggregation interval / sampling interval *
sampling interval' as the effective aggregation interval, and we don't use
floating point types. Usual users would have used aligned intervals, so
this behavioral change is not expected to make any meaningful impact, so
just make this change.
Link: https://lkml.kernel.org/r/20230914021523.60649-1-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-14 02:15:23 +00:00
|
|
|
ctx->next_aggregation_sis = ctx->passed_sample_intervals +
|
|
|
|
attrs->aggr_interval / sample_interval;
|
|
|
|
ctx->next_ops_update_sis = ctx->passed_sample_intervals +
|
|
|
|
attrs->ops_update_interval / sample_interval;
|
|
|
|
|
2023-01-19 01:38:30 +00:00
|
|
|
damon_update_monitoring_results(ctx, attrs);
|
2022-09-13 17:44:33 +00:00
|
|
|
ctx->attrs = *attrs;
|
mm/damon/core: implement scheme-specific apply interval
DAMON-based operation schemes are applied for every aggregation interval.
That was mainly because schemes were using nr_accesses, which be complete
to be used for every aggregation interval. However, the schemes are now
using nr_accesses_bp, which is updated for each sampling interval in a way
that reasonable to be used. Therefore, there is no reason to apply
schemes for each aggregation interval.
The unnecessary alignment with aggregation interval was also making some
use cases of DAMOS tricky. Quotas setting under long aggregation interval
is one such example. Suppose the aggregation interval is ten seconds, and
there is a scheme having CPU quota 100ms per 1s. The scheme will actually
uses 100ms per ten seconds, since it cannobe be applied before next
aggregation interval. The feature is working as intended, but the results
might not that intuitive for some users. This could be fixed by updating
the quota to 1s per 10s. But, in the case, the CPU usage of DAMOS could
look like spikes, and would actually make a bad effect to other
CPU-sensitive workloads.
Implement a dedicated timing interval for each DAMON-based operation
scheme, namely apply_interval. The interval will be sampling interval
aligned, and each scheme will be applied for its apply_interval. The
interval is set to 0 by default, and it means the scheme should use the
aggregation interval instead. This avoids old users getting any
behavioral difference.
Link: https://lkml.kernel.org/r/20230916020945.47296-5-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (Google) <rostedt@goodmis.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-16 02:09:40 +00:00
|
|
|
|
|
|
|
damon_for_each_scheme(s, ctx)
|
|
|
|
damos_set_next_apply_sis(s, ctx);
|
|
|
|
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
return 0;
|
|
|
|
}
|
|
|
|
|
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS)
In many cases, users might use DAMON for simple data access aware memory
management optimizations such as applying an operation scheme to a
memory region of a specific size having a specific access frequency for
a specific time. For example, "page out a memory region larger than 100
MiB but having a low access frequency more than 10 minutes", or "Use THP
for a memory region larger than 2 MiB having a high access frequency for
more than 2 seconds".
Most simple form of the solution would be doing offline data access
pattern profiling using DAMON and modifying the application source code
or system configuration based on the profiling results. Or, developing
a daemon constructed with two modules (one for access monitoring and the
other for applying memory management actions via mlock(), madvise(),
sysctl, etc) is imaginable.
To avoid users spending their time for implementation of such simple
data access monitoring-based operation schemes, this makes DAMON to
handle such schemes directly. With this change, users can simply
specify their desired schemes to DAMON. Then, DAMON will automatically
apply the schemes to the user-specified target processes.
Each of the schemes is composed with conditions for filtering of the
target memory regions and desired memory management action for the
target. Specifically, the format is::
<min/max size> <min/max access frequency> <min/max age> <action>
The filtering conditions are size of memory region, number of accesses
to the region monitored by DAMON, and the age of the region. The age of
region is incremented periodically but reset when its addresses or
access frequency has significantly changed or the action of a scheme was
applied. For the action, current implementation supports a few of
madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``,
and ``NOHUGEPAGE``.
Because DAMON supports various address spaces and application of the
actions to a monitoring target region is dependent to the type of the
target address space, the application code should be implemented by each
primitives and registered to the framework. Note that this only
implements the framework part. Following commit will implement the
action applications for virtual address spaces primitives.
Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rienjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:22 +00:00
|
|
|
/**
|
|
|
|
* damon_set_schemes() - Set data access monitoring based operation schemes.
|
|
|
|
* @ctx: monitoring context
|
|
|
|
* @schemes: array of the schemes
|
|
|
|
* @nr_schemes: number of entries in @schemes
|
|
|
|
*
|
|
|
|
* This function should not be called while the kdamond of the context is
|
|
|
|
* running.
|
|
|
|
*/
|
2022-09-16 15:20:35 +00:00
|
|
|
void damon_set_schemes(struct damon_ctx *ctx, struct damos **schemes,
|
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS)
In many cases, users might use DAMON for simple data access aware memory
management optimizations such as applying an operation scheme to a
memory region of a specific size having a specific access frequency for
a specific time. For example, "page out a memory region larger than 100
MiB but having a low access frequency more than 10 minutes", or "Use THP
for a memory region larger than 2 MiB having a high access frequency for
more than 2 seconds".
Most simple form of the solution would be doing offline data access
pattern profiling using DAMON and modifying the application source code
or system configuration based on the profiling results. Or, developing
a daemon constructed with two modules (one for access monitoring and the
other for applying memory management actions via mlock(), madvise(),
sysctl, etc) is imaginable.
To avoid users spending their time for implementation of such simple
data access monitoring-based operation schemes, this makes DAMON to
handle such schemes directly. With this change, users can simply
specify their desired schemes to DAMON. Then, DAMON will automatically
apply the schemes to the user-specified target processes.
Each of the schemes is composed with conditions for filtering of the
target memory regions and desired memory management action for the
target. Specifically, the format is::
<min/max size> <min/max access frequency> <min/max age> <action>
The filtering conditions are size of memory region, number of accesses
to the region monitored by DAMON, and the age of the region. The age of
region is incremented periodically but reset when its addresses or
access frequency has significantly changed or the action of a scheme was
applied. For the action, current implementation supports a few of
madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``,
and ``NOHUGEPAGE``.
Because DAMON supports various address spaces and application of the
actions to a monitoring target region is dependent to the type of the
target address space, the application code should be implemented by each
primitives and registered to the framework. Note that this only
implements the framework part. Following commit will implement the
action applications for virtual address spaces primitives.
Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rienjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:22 +00:00
|
|
|
ssize_t nr_schemes)
|
|
|
|
{
|
|
|
|
struct damos *s, *next;
|
|
|
|
ssize_t i;
|
|
|
|
|
|
|
|
damon_for_each_scheme_safe(s, next, ctx)
|
|
|
|
damon_destroy_scheme(s);
|
|
|
|
for (i = 0; i < nr_schemes; i++)
|
|
|
|
damon_add_scheme(ctx, schemes[i]);
|
|
|
|
}
|
|
|
|
|
mm/damon: implement a debugfs-based user space interface
DAMON is designed to be used by kernel space code such as the memory
management subsystems, and therefore it provides only kernel space API.
That said, letting the user space control DAMON could provide some
benefits to them. For example, it will allow user space to analyze their
specific workloads and make their own special optimizations.
For such cases, this commit implements a simple DAMON application kernel
module, namely 'damon-dbgfs', which merely wraps the DAMON api and exports
those to the user space via the debugfs.
'damon-dbgfs' exports three files, ``attrs``, ``target_ids``, and
``monitor_on`` under its debugfs directory, ``<debugfs>/damon/``.
Attributes
----------
Users can read and write the ``sampling interval``, ``aggregation
interval``, ``regions update interval``, and min/max number of monitoring
target regions by reading from and writing to the ``attrs`` file. For
example, below commands set those values to 5 ms, 100 ms, 1,000 ms, 10,
1000 and check it again::
# cd <debugfs>/damon
# echo 5000 100000 1000000 10 1000 > attrs
# cat attrs
5000 100000 1000000 10 1000
Target IDs
----------
Some types of address spaces supports multiple monitoring target. For
example, the virtual memory address spaces monitoring can have multiple
processes as the monitoring targets. Users can set the targets by writing
relevant id values of the targets to, and get the ids of the current
targets by reading from the ``target_ids`` file. In case of the virtual
address spaces monitoring, the values should be pids of the monitoring
target processes. For example, below commands set processes having pids
42 and 4242 as the monitoring targets and check it again::
# cd <debugfs>/damon
# echo 42 4242 > target_ids
# cat target_ids
42 4242
Note that setting the target ids doesn't start the monitoring.
Turning On/Off
--------------
Setting the files as described above doesn't incur effect unless you
explicitly start the monitoring. You can start, stop, and check the
current status of the monitoring by writing to and reading from the
``monitor_on`` file. Writing ``on`` to the file starts the monitoring of
the targets with the attributes. Writing ``off`` to the file stops those.
DAMON also stops if every targets are invalidated (in case of the virtual
memory monitoring, target processes are invalidated when terminated).
Below example commands turn on, off, and check the status of DAMON::
# cd <debugfs>/damon
# echo on > monitor_on
# echo off > monitor_on
# cat monitor_on
off
Please note that you cannot write to the above-mentioned debugfs files
while the monitoring is turned on. If you write to the files while DAMON
is running, an error code such as ``-EBUSY`` will be returned.
[akpm@linux-foundation.org: remove unneeded "alloc failed" printks]
[akpm@linux-foundation.org: replace macro with static inline]
Link: https://lkml.kernel.org/r/20210716081449.22187-8-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:53 +00:00
|
|
|
/**
|
|
|
|
* damon_nr_running_ctxs() - Return number of currently running contexts.
|
|
|
|
*/
|
|
|
|
int damon_nr_running_ctxs(void)
|
|
|
|
{
|
|
|
|
int nr_ctxs;
|
|
|
|
|
|
|
|
mutex_lock(&damon_lock);
|
|
|
|
nr_ctxs = nr_running_ctxs;
|
|
|
|
mutex_unlock(&damon_lock);
|
|
|
|
|
|
|
|
return nr_ctxs;
|
|
|
|
}
|
|
|
|
|
2021-09-08 02:56:36 +00:00
|
|
|
/* Returns the size upper limit for each monitoring region */
|
|
|
|
static unsigned long damon_region_sz_limit(struct damon_ctx *ctx)
|
|
|
|
{
|
|
|
|
struct damon_target *t;
|
|
|
|
struct damon_region *r;
|
|
|
|
unsigned long sz = 0;
|
|
|
|
|
|
|
|
damon_for_each_target(t, ctx) {
|
|
|
|
damon_for_each_region(r, t)
|
2022-09-27 00:19:46 +00:00
|
|
|
sz += damon_sz_region(r);
|
2021-09-08 02:56:36 +00:00
|
|
|
}
|
|
|
|
|
2022-09-13 17:44:32 +00:00
|
|
|
if (ctx->attrs.min_nr_regions)
|
|
|
|
sz /= ctx->attrs.min_nr_regions;
|
2021-09-08 02:56:36 +00:00
|
|
|
if (sz < DAMON_MIN_REGION)
|
|
|
|
sz = DAMON_MIN_REGION;
|
|
|
|
|
|
|
|
return sz;
|
|
|
|
}
|
|
|
|
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
static int kdamond_fn(void *data);
|
|
|
|
|
|
|
|
/*
|
|
|
|
* __damon_start() - Starts monitoring with given context.
|
|
|
|
* @ctx: monitoring context
|
|
|
|
*
|
|
|
|
* This function should be called while damon_lock is hold.
|
|
|
|
*
|
|
|
|
* Return: 0 on success, negative error code otherwise.
|
|
|
|
*/
|
|
|
|
static int __damon_start(struct damon_ctx *ctx)
|
|
|
|
{
|
|
|
|
int err = -EBUSY;
|
|
|
|
|
|
|
|
mutex_lock(&ctx->kdamond_lock);
|
|
|
|
if (!ctx->kdamond) {
|
|
|
|
err = 0;
|
|
|
|
ctx->kdamond = kthread_run(kdamond_fn, ctx, "kdamond.%d",
|
|
|
|
nr_running_ctxs);
|
|
|
|
if (IS_ERR(ctx->kdamond)) {
|
|
|
|
err = PTR_ERR(ctx->kdamond);
|
2021-11-05 20:46:15 +00:00
|
|
|
ctx->kdamond = NULL;
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
}
|
|
|
|
}
|
|
|
|
mutex_unlock(&ctx->kdamond_lock);
|
|
|
|
|
|
|
|
return err;
|
|
|
|
}
|
|
|
|
|
|
|
|
/**
|
|
|
|
* damon_start() - Starts the monitorings for a given group of contexts.
|
|
|
|
* @ctxs: an array of the pointers for contexts to start monitoring
|
|
|
|
* @nr_ctxs: size of @ctxs
|
mm/damon/core: allow non-exclusive DAMON start/stop
Patch series "Introduce DAMON sysfs interface", v3.
Introduction
============
DAMON's debugfs-based user interface (DAMON_DBGFS) served very well, so
far. However, it unnecessarily depends on debugfs, while DAMON is not
aimed to be used for only debugging. Also, the interface receives
multiple values via one file. For example, schemes file receives 18
values. As a result, it is inefficient, hard to be used, and difficult to
be extended. Especially, keeping backward compatibility of user space
tools is getting only challenging. It would be better to implement
another reliable and flexible interface and deprecate DAMON_DBGFS in long
term.
For the reason, this patchset introduces a sysfs-based new user interface
of DAMON. The idea of the new interface is, using directory hierarchies
and having one dedicated file for each value. For a short example, users
can do the virtual address monitoring via the interface as below:
# cd /sys/kernel/mm/damon/admin/
# echo 1 > kdamonds/nr_kdamonds
# echo 1 > kdamonds/0/contexts/nr_contexts
# echo vaddr > kdamonds/0/contexts/0/operations
# echo 1 > kdamonds/0/contexts/0/targets/nr_targets
# echo $(pidof <workload>) > kdamonds/0/contexts/0/targets/0/pid_target
# echo on > kdamonds/0/state
A brief representation of the files hierarchy of DAMON sysfs interface is
as below. Childs are represented with indentation, directories are having
'/' suffix, and files in each directory are separated by comma.
/sys/kernel/mm/damon/admin
│ kdamonds/nr_kdamonds
│ │ 0/state,pid
│ │ │ contexts/nr_contexts
│ │ │ │ 0/operations
│ │ │ │ │ monitoring_attrs/
│ │ │ │ │ │ intervals/sample_us,aggr_us,update_us
│ │ │ │ │ │ nr_regions/min,max
│ │ │ │ │ targets/nr_targets
│ │ │ │ │ │ 0/pid_target
│ │ │ │ │ │ │ regions/nr_regions
│ │ │ │ │ │ │ │ 0/start,end
│ │ │ │ │ │ │ │ ...
│ │ │ │ │ │ ...
│ │ │ │ │ schemes/nr_schemes
│ │ │ │ │ │ 0/action
│ │ │ │ │ │ │ access_pattern/
│ │ │ │ │ │ │ │ sz/min,max
│ │ │ │ │ │ │ │ nr_accesses/min,max
│ │ │ │ │ │ │ │ age/min,max
│ │ │ │ │ │ │ quotas/ms,bytes,reset_interval_ms
│ │ │ │ │ │ │ │ weights/sz_permil,nr_accesses_permil,age_permil
│ │ │ │ │ │ │ watermarks/metric,interval_us,high,mid,low
│ │ │ │ │ │ │ stats/nr_tried,sz_tried,nr_applied,sz_applied,qt_exceeds
│ │ │ │ │ │ ...
│ │ │ │ ...
│ │ ...
Detailed usage of the files will be described in the final Documentation
patch of this patchset.
Main Difference Between DAMON_DBGFS and DAMON_SYSFS
---------------------------------------------------
At the moment, DAMON_DBGFS and DAMON_SYSFS provides same features. One
important difference between them is their exclusiveness. DAMON_DBGFS
works in an exclusive manner, so that no DAMON worker thread (kdamond) in
the system can run concurrently and interfere somehow. For the reason,
DAMON_DBGFS asks users to construct all monitoring contexts and start them
at once. It's not a big problem but makes the operation a little bit
complex and unflexible.
For more flexible usage, DAMON_SYSFS moves the responsibility of
preventing any possible interference to the admins and work in a
non-exclusive manner. That is, users can configure and start contexts one
by one. Note that DAMON respects both exclusive groups and non-exclusive
groups of contexts, in a manner similar to that of reader-writer locks.
That is, if any exclusive monitoring contexts (e.g., contexts that started
via DAMON_DBGFS) are running, DAMON_SYSFS does not start new contexts, and
vice versa.
Future Plan of DAMON_DBGFS Deprecation
======================================
Once this patchset is merged, DAMON_DBGFS development will be frozen.
That is, we will maintain it to work as is now so that no users will be
break. But, it will not be extended to provide any new feature of DAMON.
The support will be continued only until next LTS release. After that, we
will drop DAMON_DBGFS.
User-space Tooling Compatibility
--------------------------------
As DAMON_SYSFS provides all features of DAMON_DBGFS, all user space
tooling can move to DAMON_SYSFS. As we will continue supporting
DAMON_DBGFS until next LTS kernel release, user space tools would have
enough time to move to DAMON_SYSFS.
The official user space tool, damo[1], is already supporting both
DAMON_SYSFS and DAMON_DBGFS. Both correctness tests[2] and performance
tests[3] of DAMON using DAMON_SYSFS also passed.
[1] https://github.com/awslabs/damo
[2] https://github.com/awslabs/damon-tests/tree/master/corr
[3] https://github.com/awslabs/damon-tests/tree/master/perf
Sequence of Patches
===================
First two patches (patches 1-2) make core changes for DAMON_SYSFS. The
first one (patch 1) allows non-exclusive DAMON contexts so that
DAMON_SYSFS can work in non-exclusive mode, while the second one (patch 2)
adds size of DAMON enum types so that DAMON API users can safely iterate
the enums.
Third patch (patch 3) implements basic sysfs stub for virtual address
spaces monitoring. Note that this implements only sysfs files and DAMON
is not linked. Fourth patch (patch 4) links the DAMON_SYSFS to DAMON so
that users can control DAMON using the sysfs files.
Following six patches (patches 5-10) implements other DAMON features that
DAMON_DBGFS supports one by one (physical address space monitoring,
DAMON-based operation schemes, schemes quotas, schemes prioritization
weights, schemes watermarks, and schemes stats).
Following patch (patch 11) adds a simple selftest for DAMON_SYSFS, and the
final one (patch 12) documents DAMON_SYSFS.
This patch (of 13):
To avoid interference between DAMON contexts monitoring overlapping memory
regions, damon_start() works in an exclusive manner. That is,
damon_start() does nothing bug fails if any context that started by
another instance of the function is still running. This makes its usage a
little bit restrictive. However, admins could aware each DAMON usage and
address such interferences on their own in some cases.
This commit hence implements non-exclusive mode of the function and allows
the callers to select the mode. Note that the exclusive groups and
non-exclusive groups of contexts will respect each other in a manner
similar to that of reader-writer locks. Therefore, this commit will not
cause any behavioral change to the exclusive groups.
Link: https://lkml.kernel.org/r/20220228081314.5770-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20220228081314.5770-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <skhan@linuxfoundation.org>
Cc: David Rientjes <rientjes@google.com>
Cc: Xin Hao <xhao@linux.alibaba.com>
Cc: Greg Kroah-Hartman <gregkh@linuxfoundation.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-22 21:49:21 +00:00
|
|
|
* @exclusive: exclusiveness of this contexts group
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
*
|
|
|
|
* This function starts a group of monitoring threads for a group of monitoring
|
|
|
|
* contexts. One thread per each context is created and run in parallel. The
|
mm/damon/core: allow non-exclusive DAMON start/stop
Patch series "Introduce DAMON sysfs interface", v3.
Introduction
============
DAMON's debugfs-based user interface (DAMON_DBGFS) served very well, so
far. However, it unnecessarily depends on debugfs, while DAMON is not
aimed to be used for only debugging. Also, the interface receives
multiple values via one file. For example, schemes file receives 18
values. As a result, it is inefficient, hard to be used, and difficult to
be extended. Especially, keeping backward compatibility of user space
tools is getting only challenging. It would be better to implement
another reliable and flexible interface and deprecate DAMON_DBGFS in long
term.
For the reason, this patchset introduces a sysfs-based new user interface
of DAMON. The idea of the new interface is, using directory hierarchies
and having one dedicated file for each value. For a short example, users
can do the virtual address monitoring via the interface as below:
# cd /sys/kernel/mm/damon/admin/
# echo 1 > kdamonds/nr_kdamonds
# echo 1 > kdamonds/0/contexts/nr_contexts
# echo vaddr > kdamonds/0/contexts/0/operations
# echo 1 > kdamonds/0/contexts/0/targets/nr_targets
# echo $(pidof <workload>) > kdamonds/0/contexts/0/targets/0/pid_target
# echo on > kdamonds/0/state
A brief representation of the files hierarchy of DAMON sysfs interface is
as below. Childs are represented with indentation, directories are having
'/' suffix, and files in each directory are separated by comma.
/sys/kernel/mm/damon/admin
│ kdamonds/nr_kdamonds
│ │ 0/state,pid
│ │ │ contexts/nr_contexts
│ │ │ │ 0/operations
│ │ │ │ │ monitoring_attrs/
│ │ │ │ │ │ intervals/sample_us,aggr_us,update_us
│ │ │ │ │ │ nr_regions/min,max
│ │ │ │ │ targets/nr_targets
│ │ │ │ │ │ 0/pid_target
│ │ │ │ │ │ │ regions/nr_regions
│ │ │ │ │ │ │ │ 0/start,end
│ │ │ │ │ │ │ │ ...
│ │ │ │ │ │ ...
│ │ │ │ │ schemes/nr_schemes
│ │ │ │ │ │ 0/action
│ │ │ │ │ │ │ access_pattern/
│ │ │ │ │ │ │ │ sz/min,max
│ │ │ │ │ │ │ │ nr_accesses/min,max
│ │ │ │ │ │ │ │ age/min,max
│ │ │ │ │ │ │ quotas/ms,bytes,reset_interval_ms
│ │ │ │ │ │ │ │ weights/sz_permil,nr_accesses_permil,age_permil
│ │ │ │ │ │ │ watermarks/metric,interval_us,high,mid,low
│ │ │ │ │ │ │ stats/nr_tried,sz_tried,nr_applied,sz_applied,qt_exceeds
│ │ │ │ │ │ ...
│ │ │ │ ...
│ │ ...
Detailed usage of the files will be described in the final Documentation
patch of this patchset.
Main Difference Between DAMON_DBGFS and DAMON_SYSFS
---------------------------------------------------
At the moment, DAMON_DBGFS and DAMON_SYSFS provides same features. One
important difference between them is their exclusiveness. DAMON_DBGFS
works in an exclusive manner, so that no DAMON worker thread (kdamond) in
the system can run concurrently and interfere somehow. For the reason,
DAMON_DBGFS asks users to construct all monitoring contexts and start them
at once. It's not a big problem but makes the operation a little bit
complex and unflexible.
For more flexible usage, DAMON_SYSFS moves the responsibility of
preventing any possible interference to the admins and work in a
non-exclusive manner. That is, users can configure and start contexts one
by one. Note that DAMON respects both exclusive groups and non-exclusive
groups of contexts, in a manner similar to that of reader-writer locks.
That is, if any exclusive monitoring contexts (e.g., contexts that started
via DAMON_DBGFS) are running, DAMON_SYSFS does not start new contexts, and
vice versa.
Future Plan of DAMON_DBGFS Deprecation
======================================
Once this patchset is merged, DAMON_DBGFS development will be frozen.
That is, we will maintain it to work as is now so that no users will be
break. But, it will not be extended to provide any new feature of DAMON.
The support will be continued only until next LTS release. After that, we
will drop DAMON_DBGFS.
User-space Tooling Compatibility
--------------------------------
As DAMON_SYSFS provides all features of DAMON_DBGFS, all user space
tooling can move to DAMON_SYSFS. As we will continue supporting
DAMON_DBGFS until next LTS kernel release, user space tools would have
enough time to move to DAMON_SYSFS.
The official user space tool, damo[1], is already supporting both
DAMON_SYSFS and DAMON_DBGFS. Both correctness tests[2] and performance
tests[3] of DAMON using DAMON_SYSFS also passed.
[1] https://github.com/awslabs/damo
[2] https://github.com/awslabs/damon-tests/tree/master/corr
[3] https://github.com/awslabs/damon-tests/tree/master/perf
Sequence of Patches
===================
First two patches (patches 1-2) make core changes for DAMON_SYSFS. The
first one (patch 1) allows non-exclusive DAMON contexts so that
DAMON_SYSFS can work in non-exclusive mode, while the second one (patch 2)
adds size of DAMON enum types so that DAMON API users can safely iterate
the enums.
Third patch (patch 3) implements basic sysfs stub for virtual address
spaces monitoring. Note that this implements only sysfs files and DAMON
is not linked. Fourth patch (patch 4) links the DAMON_SYSFS to DAMON so
that users can control DAMON using the sysfs files.
Following six patches (patches 5-10) implements other DAMON features that
DAMON_DBGFS supports one by one (physical address space monitoring,
DAMON-based operation schemes, schemes quotas, schemes prioritization
weights, schemes watermarks, and schemes stats).
Following patch (patch 11) adds a simple selftest for DAMON_SYSFS, and the
final one (patch 12) documents DAMON_SYSFS.
This patch (of 13):
To avoid interference between DAMON contexts monitoring overlapping memory
regions, damon_start() works in an exclusive manner. That is,
damon_start() does nothing bug fails if any context that started by
another instance of the function is still running. This makes its usage a
little bit restrictive. However, admins could aware each DAMON usage and
address such interferences on their own in some cases.
This commit hence implements non-exclusive mode of the function and allows
the callers to select the mode. Note that the exclusive groups and
non-exclusive groups of contexts will respect each other in a manner
similar to that of reader-writer locks. Therefore, this commit will not
cause any behavioral change to the exclusive groups.
Link: https://lkml.kernel.org/r/20220228081314.5770-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20220228081314.5770-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <skhan@linuxfoundation.org>
Cc: David Rientjes <rientjes@google.com>
Cc: Xin Hao <xhao@linux.alibaba.com>
Cc: Greg Kroah-Hartman <gregkh@linuxfoundation.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-22 21:49:21 +00:00
|
|
|
* caller should handle synchronization between the threads by itself. If
|
|
|
|
* @exclusive is true and a group of threads that created by other
|
|
|
|
* 'damon_start()' call is currently running, this function does nothing but
|
|
|
|
* returns -EBUSY.
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
*
|
|
|
|
* Return: 0 on success, negative error code otherwise.
|
|
|
|
*/
|
mm/damon/core: allow non-exclusive DAMON start/stop
Patch series "Introduce DAMON sysfs interface", v3.
Introduction
============
DAMON's debugfs-based user interface (DAMON_DBGFS) served very well, so
far. However, it unnecessarily depends on debugfs, while DAMON is not
aimed to be used for only debugging. Also, the interface receives
multiple values via one file. For example, schemes file receives 18
values. As a result, it is inefficient, hard to be used, and difficult to
be extended. Especially, keeping backward compatibility of user space
tools is getting only challenging. It would be better to implement
another reliable and flexible interface and deprecate DAMON_DBGFS in long
term.
For the reason, this patchset introduces a sysfs-based new user interface
of DAMON. The idea of the new interface is, using directory hierarchies
and having one dedicated file for each value. For a short example, users
can do the virtual address monitoring via the interface as below:
# cd /sys/kernel/mm/damon/admin/
# echo 1 > kdamonds/nr_kdamonds
# echo 1 > kdamonds/0/contexts/nr_contexts
# echo vaddr > kdamonds/0/contexts/0/operations
# echo 1 > kdamonds/0/contexts/0/targets/nr_targets
# echo $(pidof <workload>) > kdamonds/0/contexts/0/targets/0/pid_target
# echo on > kdamonds/0/state
A brief representation of the files hierarchy of DAMON sysfs interface is
as below. Childs are represented with indentation, directories are having
'/' suffix, and files in each directory are separated by comma.
/sys/kernel/mm/damon/admin
│ kdamonds/nr_kdamonds
│ │ 0/state,pid
│ │ │ contexts/nr_contexts
│ │ │ │ 0/operations
│ │ │ │ │ monitoring_attrs/
│ │ │ │ │ │ intervals/sample_us,aggr_us,update_us
│ │ │ │ │ │ nr_regions/min,max
│ │ │ │ │ targets/nr_targets
│ │ │ │ │ │ 0/pid_target
│ │ │ │ │ │ │ regions/nr_regions
│ │ │ │ │ │ │ │ 0/start,end
│ │ │ │ │ │ │ │ ...
│ │ │ │ │ │ ...
│ │ │ │ │ schemes/nr_schemes
│ │ │ │ │ │ 0/action
│ │ │ │ │ │ │ access_pattern/
│ │ │ │ │ │ │ │ sz/min,max
│ │ │ │ │ │ │ │ nr_accesses/min,max
│ │ │ │ │ │ │ │ age/min,max
│ │ │ │ │ │ │ quotas/ms,bytes,reset_interval_ms
│ │ │ │ │ │ │ │ weights/sz_permil,nr_accesses_permil,age_permil
│ │ │ │ │ │ │ watermarks/metric,interval_us,high,mid,low
│ │ │ │ │ │ │ stats/nr_tried,sz_tried,nr_applied,sz_applied,qt_exceeds
│ │ │ │ │ │ ...
│ │ │ │ ...
│ │ ...
Detailed usage of the files will be described in the final Documentation
patch of this patchset.
Main Difference Between DAMON_DBGFS and DAMON_SYSFS
---------------------------------------------------
At the moment, DAMON_DBGFS and DAMON_SYSFS provides same features. One
important difference between them is their exclusiveness. DAMON_DBGFS
works in an exclusive manner, so that no DAMON worker thread (kdamond) in
the system can run concurrently and interfere somehow. For the reason,
DAMON_DBGFS asks users to construct all monitoring contexts and start them
at once. It's not a big problem but makes the operation a little bit
complex and unflexible.
For more flexible usage, DAMON_SYSFS moves the responsibility of
preventing any possible interference to the admins and work in a
non-exclusive manner. That is, users can configure and start contexts one
by one. Note that DAMON respects both exclusive groups and non-exclusive
groups of contexts, in a manner similar to that of reader-writer locks.
That is, if any exclusive monitoring contexts (e.g., contexts that started
via DAMON_DBGFS) are running, DAMON_SYSFS does not start new contexts, and
vice versa.
Future Plan of DAMON_DBGFS Deprecation
======================================
Once this patchset is merged, DAMON_DBGFS development will be frozen.
That is, we will maintain it to work as is now so that no users will be
break. But, it will not be extended to provide any new feature of DAMON.
The support will be continued only until next LTS release. After that, we
will drop DAMON_DBGFS.
User-space Tooling Compatibility
--------------------------------
As DAMON_SYSFS provides all features of DAMON_DBGFS, all user space
tooling can move to DAMON_SYSFS. As we will continue supporting
DAMON_DBGFS until next LTS kernel release, user space tools would have
enough time to move to DAMON_SYSFS.
The official user space tool, damo[1], is already supporting both
DAMON_SYSFS and DAMON_DBGFS. Both correctness tests[2] and performance
tests[3] of DAMON using DAMON_SYSFS also passed.
[1] https://github.com/awslabs/damo
[2] https://github.com/awslabs/damon-tests/tree/master/corr
[3] https://github.com/awslabs/damon-tests/tree/master/perf
Sequence of Patches
===================
First two patches (patches 1-2) make core changes for DAMON_SYSFS. The
first one (patch 1) allows non-exclusive DAMON contexts so that
DAMON_SYSFS can work in non-exclusive mode, while the second one (patch 2)
adds size of DAMON enum types so that DAMON API users can safely iterate
the enums.
Third patch (patch 3) implements basic sysfs stub for virtual address
spaces monitoring. Note that this implements only sysfs files and DAMON
is not linked. Fourth patch (patch 4) links the DAMON_SYSFS to DAMON so
that users can control DAMON using the sysfs files.
Following six patches (patches 5-10) implements other DAMON features that
DAMON_DBGFS supports one by one (physical address space monitoring,
DAMON-based operation schemes, schemes quotas, schemes prioritization
weights, schemes watermarks, and schemes stats).
Following patch (patch 11) adds a simple selftest for DAMON_SYSFS, and the
final one (patch 12) documents DAMON_SYSFS.
This patch (of 13):
To avoid interference between DAMON contexts monitoring overlapping memory
regions, damon_start() works in an exclusive manner. That is,
damon_start() does nothing bug fails if any context that started by
another instance of the function is still running. This makes its usage a
little bit restrictive. However, admins could aware each DAMON usage and
address such interferences on their own in some cases.
This commit hence implements non-exclusive mode of the function and allows
the callers to select the mode. Note that the exclusive groups and
non-exclusive groups of contexts will respect each other in a manner
similar to that of reader-writer locks. Therefore, this commit will not
cause any behavioral change to the exclusive groups.
Link: https://lkml.kernel.org/r/20220228081314.5770-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20220228081314.5770-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <skhan@linuxfoundation.org>
Cc: David Rientjes <rientjes@google.com>
Cc: Xin Hao <xhao@linux.alibaba.com>
Cc: Greg Kroah-Hartman <gregkh@linuxfoundation.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-22 21:49:21 +00:00
|
|
|
int damon_start(struct damon_ctx **ctxs, int nr_ctxs, bool exclusive)
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
{
|
|
|
|
int i;
|
|
|
|
int err = 0;
|
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|
|
|
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|
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mutex_lock(&damon_lock);
|
mm/damon/core: allow non-exclusive DAMON start/stop
Patch series "Introduce DAMON sysfs interface", v3.
Introduction
============
DAMON's debugfs-based user interface (DAMON_DBGFS) served very well, so
far. However, it unnecessarily depends on debugfs, while DAMON is not
aimed to be used for only debugging. Also, the interface receives
multiple values via one file. For example, schemes file receives 18
values. As a result, it is inefficient, hard to be used, and difficult to
be extended. Especially, keeping backward compatibility of user space
tools is getting only challenging. It would be better to implement
another reliable and flexible interface and deprecate DAMON_DBGFS in long
term.
For the reason, this patchset introduces a sysfs-based new user interface
of DAMON. The idea of the new interface is, using directory hierarchies
and having one dedicated file for each value. For a short example, users
can do the virtual address monitoring via the interface as below:
# cd /sys/kernel/mm/damon/admin/
# echo 1 > kdamonds/nr_kdamonds
# echo 1 > kdamonds/0/contexts/nr_contexts
# echo vaddr > kdamonds/0/contexts/0/operations
# echo 1 > kdamonds/0/contexts/0/targets/nr_targets
# echo $(pidof <workload>) > kdamonds/0/contexts/0/targets/0/pid_target
# echo on > kdamonds/0/state
A brief representation of the files hierarchy of DAMON sysfs interface is
as below. Childs are represented with indentation, directories are having
'/' suffix, and files in each directory are separated by comma.
/sys/kernel/mm/damon/admin
│ kdamonds/nr_kdamonds
│ │ 0/state,pid
│ │ │ contexts/nr_contexts
│ │ │ │ 0/operations
│ │ │ │ │ monitoring_attrs/
│ │ │ │ │ │ intervals/sample_us,aggr_us,update_us
│ │ │ │ │ │ nr_regions/min,max
│ │ │ │ │ targets/nr_targets
│ │ │ │ │ │ 0/pid_target
│ │ │ │ │ │ │ regions/nr_regions
│ │ │ │ │ │ │ │ 0/start,end
│ │ │ │ │ │ │ │ ...
│ │ │ │ │ │ ...
│ │ │ │ │ schemes/nr_schemes
│ │ │ │ │ │ 0/action
│ │ │ │ │ │ │ access_pattern/
│ │ │ │ │ │ │ │ sz/min,max
│ │ │ │ │ │ │ │ nr_accesses/min,max
│ │ │ │ │ │ │ │ age/min,max
│ │ │ │ │ │ │ quotas/ms,bytes,reset_interval_ms
│ │ │ │ │ │ │ │ weights/sz_permil,nr_accesses_permil,age_permil
│ │ │ │ │ │ │ watermarks/metric,interval_us,high,mid,low
│ │ │ │ │ │ │ stats/nr_tried,sz_tried,nr_applied,sz_applied,qt_exceeds
│ │ │ │ │ │ ...
│ │ │ │ ...
│ │ ...
Detailed usage of the files will be described in the final Documentation
patch of this patchset.
Main Difference Between DAMON_DBGFS and DAMON_SYSFS
---------------------------------------------------
At the moment, DAMON_DBGFS and DAMON_SYSFS provides same features. One
important difference between them is their exclusiveness. DAMON_DBGFS
works in an exclusive manner, so that no DAMON worker thread (kdamond) in
the system can run concurrently and interfere somehow. For the reason,
DAMON_DBGFS asks users to construct all monitoring contexts and start them
at once. It's not a big problem but makes the operation a little bit
complex and unflexible.
For more flexible usage, DAMON_SYSFS moves the responsibility of
preventing any possible interference to the admins and work in a
non-exclusive manner. That is, users can configure and start contexts one
by one. Note that DAMON respects both exclusive groups and non-exclusive
groups of contexts, in a manner similar to that of reader-writer locks.
That is, if any exclusive monitoring contexts (e.g., contexts that started
via DAMON_DBGFS) are running, DAMON_SYSFS does not start new contexts, and
vice versa.
Future Plan of DAMON_DBGFS Deprecation
======================================
Once this patchset is merged, DAMON_DBGFS development will be frozen.
That is, we will maintain it to work as is now so that no users will be
break. But, it will not be extended to provide any new feature of DAMON.
The support will be continued only until next LTS release. After that, we
will drop DAMON_DBGFS.
User-space Tooling Compatibility
--------------------------------
As DAMON_SYSFS provides all features of DAMON_DBGFS, all user space
tooling can move to DAMON_SYSFS. As we will continue supporting
DAMON_DBGFS until next LTS kernel release, user space tools would have
enough time to move to DAMON_SYSFS.
The official user space tool, damo[1], is already supporting both
DAMON_SYSFS and DAMON_DBGFS. Both correctness tests[2] and performance
tests[3] of DAMON using DAMON_SYSFS also passed.
[1] https://github.com/awslabs/damo
[2] https://github.com/awslabs/damon-tests/tree/master/corr
[3] https://github.com/awslabs/damon-tests/tree/master/perf
Sequence of Patches
===================
First two patches (patches 1-2) make core changes for DAMON_SYSFS. The
first one (patch 1) allows non-exclusive DAMON contexts so that
DAMON_SYSFS can work in non-exclusive mode, while the second one (patch 2)
adds size of DAMON enum types so that DAMON API users can safely iterate
the enums.
Third patch (patch 3) implements basic sysfs stub for virtual address
spaces monitoring. Note that this implements only sysfs files and DAMON
is not linked. Fourth patch (patch 4) links the DAMON_SYSFS to DAMON so
that users can control DAMON using the sysfs files.
Following six patches (patches 5-10) implements other DAMON features that
DAMON_DBGFS supports one by one (physical address space monitoring,
DAMON-based operation schemes, schemes quotas, schemes prioritization
weights, schemes watermarks, and schemes stats).
Following patch (patch 11) adds a simple selftest for DAMON_SYSFS, and the
final one (patch 12) documents DAMON_SYSFS.
This patch (of 13):
To avoid interference between DAMON contexts monitoring overlapping memory
regions, damon_start() works in an exclusive manner. That is,
damon_start() does nothing bug fails if any context that started by
another instance of the function is still running. This makes its usage a
little bit restrictive. However, admins could aware each DAMON usage and
address such interferences on their own in some cases.
This commit hence implements non-exclusive mode of the function and allows
the callers to select the mode. Note that the exclusive groups and
non-exclusive groups of contexts will respect each other in a manner
similar to that of reader-writer locks. Therefore, this commit will not
cause any behavioral change to the exclusive groups.
Link: https://lkml.kernel.org/r/20220228081314.5770-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20220228081314.5770-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <skhan@linuxfoundation.org>
Cc: David Rientjes <rientjes@google.com>
Cc: Xin Hao <xhao@linux.alibaba.com>
Cc: Greg Kroah-Hartman <gregkh@linuxfoundation.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-22 21:49:21 +00:00
|
|
|
if ((exclusive && nr_running_ctxs) ||
|
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(!exclusive && running_exclusive_ctxs)) {
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
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mutex_unlock(&damon_lock);
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return -EBUSY;
|
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}
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for (i = 0; i < nr_ctxs; i++) {
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err = __damon_start(ctxs[i]);
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if (err)
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break;
|
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nr_running_ctxs++;
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}
|
mm/damon/core: allow non-exclusive DAMON start/stop
Patch series "Introduce DAMON sysfs interface", v3.
Introduction
============
DAMON's debugfs-based user interface (DAMON_DBGFS) served very well, so
far. However, it unnecessarily depends on debugfs, while DAMON is not
aimed to be used for only debugging. Also, the interface receives
multiple values via one file. For example, schemes file receives 18
values. As a result, it is inefficient, hard to be used, and difficult to
be extended. Especially, keeping backward compatibility of user space
tools is getting only challenging. It would be better to implement
another reliable and flexible interface and deprecate DAMON_DBGFS in long
term.
For the reason, this patchset introduces a sysfs-based new user interface
of DAMON. The idea of the new interface is, using directory hierarchies
and having one dedicated file for each value. For a short example, users
can do the virtual address monitoring via the interface as below:
# cd /sys/kernel/mm/damon/admin/
# echo 1 > kdamonds/nr_kdamonds
# echo 1 > kdamonds/0/contexts/nr_contexts
# echo vaddr > kdamonds/0/contexts/0/operations
# echo 1 > kdamonds/0/contexts/0/targets/nr_targets
# echo $(pidof <workload>) > kdamonds/0/contexts/0/targets/0/pid_target
# echo on > kdamonds/0/state
A brief representation of the files hierarchy of DAMON sysfs interface is
as below. Childs are represented with indentation, directories are having
'/' suffix, and files in each directory are separated by comma.
/sys/kernel/mm/damon/admin
│ kdamonds/nr_kdamonds
│ │ 0/state,pid
│ │ │ contexts/nr_contexts
│ │ │ │ 0/operations
│ │ │ │ │ monitoring_attrs/
│ │ │ │ │ │ intervals/sample_us,aggr_us,update_us
│ │ │ │ │ │ nr_regions/min,max
│ │ │ │ │ targets/nr_targets
│ │ │ │ │ │ 0/pid_target
│ │ │ │ │ │ │ regions/nr_regions
│ │ │ │ │ │ │ │ 0/start,end
│ │ │ │ │ │ │ │ ...
│ │ │ │ │ │ ...
│ │ │ │ │ schemes/nr_schemes
│ │ │ │ │ │ 0/action
│ │ │ │ │ │ │ access_pattern/
│ │ │ │ │ │ │ │ sz/min,max
│ │ │ │ │ │ │ │ nr_accesses/min,max
│ │ │ │ │ │ │ │ age/min,max
│ │ │ │ │ │ │ quotas/ms,bytes,reset_interval_ms
│ │ │ │ │ │ │ │ weights/sz_permil,nr_accesses_permil,age_permil
│ │ │ │ │ │ │ watermarks/metric,interval_us,high,mid,low
│ │ │ │ │ │ │ stats/nr_tried,sz_tried,nr_applied,sz_applied,qt_exceeds
│ │ │ │ │ │ ...
│ │ │ │ ...
│ │ ...
Detailed usage of the files will be described in the final Documentation
patch of this patchset.
Main Difference Between DAMON_DBGFS and DAMON_SYSFS
---------------------------------------------------
At the moment, DAMON_DBGFS and DAMON_SYSFS provides same features. One
important difference between them is their exclusiveness. DAMON_DBGFS
works in an exclusive manner, so that no DAMON worker thread (kdamond) in
the system can run concurrently and interfere somehow. For the reason,
DAMON_DBGFS asks users to construct all monitoring contexts and start them
at once. It's not a big problem but makes the operation a little bit
complex and unflexible.
For more flexible usage, DAMON_SYSFS moves the responsibility of
preventing any possible interference to the admins and work in a
non-exclusive manner. That is, users can configure and start contexts one
by one. Note that DAMON respects both exclusive groups and non-exclusive
groups of contexts, in a manner similar to that of reader-writer locks.
That is, if any exclusive monitoring contexts (e.g., contexts that started
via DAMON_DBGFS) are running, DAMON_SYSFS does not start new contexts, and
vice versa.
Future Plan of DAMON_DBGFS Deprecation
======================================
Once this patchset is merged, DAMON_DBGFS development will be frozen.
That is, we will maintain it to work as is now so that no users will be
break. But, it will not be extended to provide any new feature of DAMON.
The support will be continued only until next LTS release. After that, we
will drop DAMON_DBGFS.
User-space Tooling Compatibility
--------------------------------
As DAMON_SYSFS provides all features of DAMON_DBGFS, all user space
tooling can move to DAMON_SYSFS. As we will continue supporting
DAMON_DBGFS until next LTS kernel release, user space tools would have
enough time to move to DAMON_SYSFS.
The official user space tool, damo[1], is already supporting both
DAMON_SYSFS and DAMON_DBGFS. Both correctness tests[2] and performance
tests[3] of DAMON using DAMON_SYSFS also passed.
[1] https://github.com/awslabs/damo
[2] https://github.com/awslabs/damon-tests/tree/master/corr
[3] https://github.com/awslabs/damon-tests/tree/master/perf
Sequence of Patches
===================
First two patches (patches 1-2) make core changes for DAMON_SYSFS. The
first one (patch 1) allows non-exclusive DAMON contexts so that
DAMON_SYSFS can work in non-exclusive mode, while the second one (patch 2)
adds size of DAMON enum types so that DAMON API users can safely iterate
the enums.
Third patch (patch 3) implements basic sysfs stub for virtual address
spaces monitoring. Note that this implements only sysfs files and DAMON
is not linked. Fourth patch (patch 4) links the DAMON_SYSFS to DAMON so
that users can control DAMON using the sysfs files.
Following six patches (patches 5-10) implements other DAMON features that
DAMON_DBGFS supports one by one (physical address space monitoring,
DAMON-based operation schemes, schemes quotas, schemes prioritization
weights, schemes watermarks, and schemes stats).
Following patch (patch 11) adds a simple selftest for DAMON_SYSFS, and the
final one (patch 12) documents DAMON_SYSFS.
This patch (of 13):
To avoid interference between DAMON contexts monitoring overlapping memory
regions, damon_start() works in an exclusive manner. That is,
damon_start() does nothing bug fails if any context that started by
another instance of the function is still running. This makes its usage a
little bit restrictive. However, admins could aware each DAMON usage and
address such interferences on their own in some cases.
This commit hence implements non-exclusive mode of the function and allows
the callers to select the mode. Note that the exclusive groups and
non-exclusive groups of contexts will respect each other in a manner
similar to that of reader-writer locks. Therefore, this commit will not
cause any behavioral change to the exclusive groups.
Link: https://lkml.kernel.org/r/20220228081314.5770-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20220228081314.5770-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <skhan@linuxfoundation.org>
Cc: David Rientjes <rientjes@google.com>
Cc: Xin Hao <xhao@linux.alibaba.com>
Cc: Greg Kroah-Hartman <gregkh@linuxfoundation.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-22 21:49:21 +00:00
|
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|
if (exclusive && nr_running_ctxs)
|
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running_exclusive_ctxs = true;
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
mutex_unlock(&damon_lock);
|
|
|
|
|
|
|
|
return err;
|
|
|
|
}
|
|
|
|
|
|
|
|
/*
|
mm/damon/core: allow non-exclusive DAMON start/stop
Patch series "Introduce DAMON sysfs interface", v3.
Introduction
============
DAMON's debugfs-based user interface (DAMON_DBGFS) served very well, so
far. However, it unnecessarily depends on debugfs, while DAMON is not
aimed to be used for only debugging. Also, the interface receives
multiple values via one file. For example, schemes file receives 18
values. As a result, it is inefficient, hard to be used, and difficult to
be extended. Especially, keeping backward compatibility of user space
tools is getting only challenging. It would be better to implement
another reliable and flexible interface and deprecate DAMON_DBGFS in long
term.
For the reason, this patchset introduces a sysfs-based new user interface
of DAMON. The idea of the new interface is, using directory hierarchies
and having one dedicated file for each value. For a short example, users
can do the virtual address monitoring via the interface as below:
# cd /sys/kernel/mm/damon/admin/
# echo 1 > kdamonds/nr_kdamonds
# echo 1 > kdamonds/0/contexts/nr_contexts
# echo vaddr > kdamonds/0/contexts/0/operations
# echo 1 > kdamonds/0/contexts/0/targets/nr_targets
# echo $(pidof <workload>) > kdamonds/0/contexts/0/targets/0/pid_target
# echo on > kdamonds/0/state
A brief representation of the files hierarchy of DAMON sysfs interface is
as below. Childs are represented with indentation, directories are having
'/' suffix, and files in each directory are separated by comma.
/sys/kernel/mm/damon/admin
│ kdamonds/nr_kdamonds
│ │ 0/state,pid
│ │ │ contexts/nr_contexts
│ │ │ │ 0/operations
│ │ │ │ │ monitoring_attrs/
│ │ │ │ │ │ intervals/sample_us,aggr_us,update_us
│ │ │ │ │ │ nr_regions/min,max
│ │ │ │ │ targets/nr_targets
│ │ │ │ │ │ 0/pid_target
│ │ │ │ │ │ │ regions/nr_regions
│ │ │ │ │ │ │ │ 0/start,end
│ │ │ │ │ │ │ │ ...
│ │ │ │ │ │ ...
│ │ │ │ │ schemes/nr_schemes
│ │ │ │ │ │ 0/action
│ │ │ │ │ │ │ access_pattern/
│ │ │ │ │ │ │ │ sz/min,max
│ │ │ │ │ │ │ │ nr_accesses/min,max
│ │ │ │ │ │ │ │ age/min,max
│ │ │ │ │ │ │ quotas/ms,bytes,reset_interval_ms
│ │ │ │ │ │ │ │ weights/sz_permil,nr_accesses_permil,age_permil
│ │ │ │ │ │ │ watermarks/metric,interval_us,high,mid,low
│ │ │ │ │ │ │ stats/nr_tried,sz_tried,nr_applied,sz_applied,qt_exceeds
│ │ │ │ │ │ ...
│ │ │ │ ...
│ │ ...
Detailed usage of the files will be described in the final Documentation
patch of this patchset.
Main Difference Between DAMON_DBGFS and DAMON_SYSFS
---------------------------------------------------
At the moment, DAMON_DBGFS and DAMON_SYSFS provides same features. One
important difference between them is their exclusiveness. DAMON_DBGFS
works in an exclusive manner, so that no DAMON worker thread (kdamond) in
the system can run concurrently and interfere somehow. For the reason,
DAMON_DBGFS asks users to construct all monitoring contexts and start them
at once. It's not a big problem but makes the operation a little bit
complex and unflexible.
For more flexible usage, DAMON_SYSFS moves the responsibility of
preventing any possible interference to the admins and work in a
non-exclusive manner. That is, users can configure and start contexts one
by one. Note that DAMON respects both exclusive groups and non-exclusive
groups of contexts, in a manner similar to that of reader-writer locks.
That is, if any exclusive monitoring contexts (e.g., contexts that started
via DAMON_DBGFS) are running, DAMON_SYSFS does not start new contexts, and
vice versa.
Future Plan of DAMON_DBGFS Deprecation
======================================
Once this patchset is merged, DAMON_DBGFS development will be frozen.
That is, we will maintain it to work as is now so that no users will be
break. But, it will not be extended to provide any new feature of DAMON.
The support will be continued only until next LTS release. After that, we
will drop DAMON_DBGFS.
User-space Tooling Compatibility
--------------------------------
As DAMON_SYSFS provides all features of DAMON_DBGFS, all user space
tooling can move to DAMON_SYSFS. As we will continue supporting
DAMON_DBGFS until next LTS kernel release, user space tools would have
enough time to move to DAMON_SYSFS.
The official user space tool, damo[1], is already supporting both
DAMON_SYSFS and DAMON_DBGFS. Both correctness tests[2] and performance
tests[3] of DAMON using DAMON_SYSFS also passed.
[1] https://github.com/awslabs/damo
[2] https://github.com/awslabs/damon-tests/tree/master/corr
[3] https://github.com/awslabs/damon-tests/tree/master/perf
Sequence of Patches
===================
First two patches (patches 1-2) make core changes for DAMON_SYSFS. The
first one (patch 1) allows non-exclusive DAMON contexts so that
DAMON_SYSFS can work in non-exclusive mode, while the second one (patch 2)
adds size of DAMON enum types so that DAMON API users can safely iterate
the enums.
Third patch (patch 3) implements basic sysfs stub for virtual address
spaces monitoring. Note that this implements only sysfs files and DAMON
is not linked. Fourth patch (patch 4) links the DAMON_SYSFS to DAMON so
that users can control DAMON using the sysfs files.
Following six patches (patches 5-10) implements other DAMON features that
DAMON_DBGFS supports one by one (physical address space monitoring,
DAMON-based operation schemes, schemes quotas, schemes prioritization
weights, schemes watermarks, and schemes stats).
Following patch (patch 11) adds a simple selftest for DAMON_SYSFS, and the
final one (patch 12) documents DAMON_SYSFS.
This patch (of 13):
To avoid interference between DAMON contexts monitoring overlapping memory
regions, damon_start() works in an exclusive manner. That is,
damon_start() does nothing bug fails if any context that started by
another instance of the function is still running. This makes its usage a
little bit restrictive. However, admins could aware each DAMON usage and
address such interferences on their own in some cases.
This commit hence implements non-exclusive mode of the function and allows
the callers to select the mode. Note that the exclusive groups and
non-exclusive groups of contexts will respect each other in a manner
similar to that of reader-writer locks. Therefore, this commit will not
cause any behavioral change to the exclusive groups.
Link: https://lkml.kernel.org/r/20220228081314.5770-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20220228081314.5770-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <skhan@linuxfoundation.org>
Cc: David Rientjes <rientjes@google.com>
Cc: Xin Hao <xhao@linux.alibaba.com>
Cc: Greg Kroah-Hartman <gregkh@linuxfoundation.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-22 21:49:21 +00:00
|
|
|
* __damon_stop() - Stops monitoring of a given context.
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
* @ctx: monitoring context
|
|
|
|
*
|
|
|
|
* Return: 0 on success, negative error code otherwise.
|
|
|
|
*/
|
|
|
|
static int __damon_stop(struct damon_ctx *ctx)
|
|
|
|
{
|
2021-11-05 20:48:22 +00:00
|
|
|
struct task_struct *tsk;
|
|
|
|
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
mutex_lock(&ctx->kdamond_lock);
|
2021-11-05 20:48:22 +00:00
|
|
|
tsk = ctx->kdamond;
|
|
|
|
if (tsk) {
|
|
|
|
get_task_struct(tsk);
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
mutex_unlock(&ctx->kdamond_lock);
|
2023-09-07 23:40:48 +00:00
|
|
|
kthread_stop_put(tsk);
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
return 0;
|
|
|
|
}
|
|
|
|
mutex_unlock(&ctx->kdamond_lock);
|
|
|
|
|
|
|
|
return -EPERM;
|
|
|
|
}
|
|
|
|
|
|
|
|
/**
|
|
|
|
* damon_stop() - Stops the monitorings for a given group of contexts.
|
|
|
|
* @ctxs: an array of the pointers for contexts to stop monitoring
|
|
|
|
* @nr_ctxs: size of @ctxs
|
|
|
|
*
|
|
|
|
* Return: 0 on success, negative error code otherwise.
|
|
|
|
*/
|
|
|
|
int damon_stop(struct damon_ctx **ctxs, int nr_ctxs)
|
|
|
|
{
|
|
|
|
int i, err = 0;
|
|
|
|
|
|
|
|
for (i = 0; i < nr_ctxs; i++) {
|
|
|
|
/* nr_running_ctxs is decremented in kdamond_fn */
|
|
|
|
err = __damon_stop(ctxs[i]);
|
|
|
|
if (err)
|
mm/damon/core: allow non-exclusive DAMON start/stop
Patch series "Introduce DAMON sysfs interface", v3.
Introduction
============
DAMON's debugfs-based user interface (DAMON_DBGFS) served very well, so
far. However, it unnecessarily depends on debugfs, while DAMON is not
aimed to be used for only debugging. Also, the interface receives
multiple values via one file. For example, schemes file receives 18
values. As a result, it is inefficient, hard to be used, and difficult to
be extended. Especially, keeping backward compatibility of user space
tools is getting only challenging. It would be better to implement
another reliable and flexible interface and deprecate DAMON_DBGFS in long
term.
For the reason, this patchset introduces a sysfs-based new user interface
of DAMON. The idea of the new interface is, using directory hierarchies
and having one dedicated file for each value. For a short example, users
can do the virtual address monitoring via the interface as below:
# cd /sys/kernel/mm/damon/admin/
# echo 1 > kdamonds/nr_kdamonds
# echo 1 > kdamonds/0/contexts/nr_contexts
# echo vaddr > kdamonds/0/contexts/0/operations
# echo 1 > kdamonds/0/contexts/0/targets/nr_targets
# echo $(pidof <workload>) > kdamonds/0/contexts/0/targets/0/pid_target
# echo on > kdamonds/0/state
A brief representation of the files hierarchy of DAMON sysfs interface is
as below. Childs are represented with indentation, directories are having
'/' suffix, and files in each directory are separated by comma.
/sys/kernel/mm/damon/admin
│ kdamonds/nr_kdamonds
│ │ 0/state,pid
│ │ │ contexts/nr_contexts
│ │ │ │ 0/operations
│ │ │ │ │ monitoring_attrs/
│ │ │ │ │ │ intervals/sample_us,aggr_us,update_us
│ │ │ │ │ │ nr_regions/min,max
│ │ │ │ │ targets/nr_targets
│ │ │ │ │ │ 0/pid_target
│ │ │ │ │ │ │ regions/nr_regions
│ │ │ │ │ │ │ │ 0/start,end
│ │ │ │ │ │ │ │ ...
│ │ │ │ │ │ ...
│ │ │ │ │ schemes/nr_schemes
│ │ │ │ │ │ 0/action
│ │ │ │ │ │ │ access_pattern/
│ │ │ │ │ │ │ │ sz/min,max
│ │ │ │ │ │ │ │ nr_accesses/min,max
│ │ │ │ │ │ │ │ age/min,max
│ │ │ │ │ │ │ quotas/ms,bytes,reset_interval_ms
│ │ │ │ │ │ │ │ weights/sz_permil,nr_accesses_permil,age_permil
│ │ │ │ │ │ │ watermarks/metric,interval_us,high,mid,low
│ │ │ │ │ │ │ stats/nr_tried,sz_tried,nr_applied,sz_applied,qt_exceeds
│ │ │ │ │ │ ...
│ │ │ │ ...
│ │ ...
Detailed usage of the files will be described in the final Documentation
patch of this patchset.
Main Difference Between DAMON_DBGFS and DAMON_SYSFS
---------------------------------------------------
At the moment, DAMON_DBGFS and DAMON_SYSFS provides same features. One
important difference between them is their exclusiveness. DAMON_DBGFS
works in an exclusive manner, so that no DAMON worker thread (kdamond) in
the system can run concurrently and interfere somehow. For the reason,
DAMON_DBGFS asks users to construct all monitoring contexts and start them
at once. It's not a big problem but makes the operation a little bit
complex and unflexible.
For more flexible usage, DAMON_SYSFS moves the responsibility of
preventing any possible interference to the admins and work in a
non-exclusive manner. That is, users can configure and start contexts one
by one. Note that DAMON respects both exclusive groups and non-exclusive
groups of contexts, in a manner similar to that of reader-writer locks.
That is, if any exclusive monitoring contexts (e.g., contexts that started
via DAMON_DBGFS) are running, DAMON_SYSFS does not start new contexts, and
vice versa.
Future Plan of DAMON_DBGFS Deprecation
======================================
Once this patchset is merged, DAMON_DBGFS development will be frozen.
That is, we will maintain it to work as is now so that no users will be
break. But, it will not be extended to provide any new feature of DAMON.
The support will be continued only until next LTS release. After that, we
will drop DAMON_DBGFS.
User-space Tooling Compatibility
--------------------------------
As DAMON_SYSFS provides all features of DAMON_DBGFS, all user space
tooling can move to DAMON_SYSFS. As we will continue supporting
DAMON_DBGFS until next LTS kernel release, user space tools would have
enough time to move to DAMON_SYSFS.
The official user space tool, damo[1], is already supporting both
DAMON_SYSFS and DAMON_DBGFS. Both correctness tests[2] and performance
tests[3] of DAMON using DAMON_SYSFS also passed.
[1] https://github.com/awslabs/damo
[2] https://github.com/awslabs/damon-tests/tree/master/corr
[3] https://github.com/awslabs/damon-tests/tree/master/perf
Sequence of Patches
===================
First two patches (patches 1-2) make core changes for DAMON_SYSFS. The
first one (patch 1) allows non-exclusive DAMON contexts so that
DAMON_SYSFS can work in non-exclusive mode, while the second one (patch 2)
adds size of DAMON enum types so that DAMON API users can safely iterate
the enums.
Third patch (patch 3) implements basic sysfs stub for virtual address
spaces monitoring. Note that this implements only sysfs files and DAMON
is not linked. Fourth patch (patch 4) links the DAMON_SYSFS to DAMON so
that users can control DAMON using the sysfs files.
Following six patches (patches 5-10) implements other DAMON features that
DAMON_DBGFS supports one by one (physical address space monitoring,
DAMON-based operation schemes, schemes quotas, schemes prioritization
weights, schemes watermarks, and schemes stats).
Following patch (patch 11) adds a simple selftest for DAMON_SYSFS, and the
final one (patch 12) documents DAMON_SYSFS.
This patch (of 13):
To avoid interference between DAMON contexts monitoring overlapping memory
regions, damon_start() works in an exclusive manner. That is,
damon_start() does nothing bug fails if any context that started by
another instance of the function is still running. This makes its usage a
little bit restrictive. However, admins could aware each DAMON usage and
address such interferences on their own in some cases.
This commit hence implements non-exclusive mode of the function and allows
the callers to select the mode. Note that the exclusive groups and
non-exclusive groups of contexts will respect each other in a manner
similar to that of reader-writer locks. Therefore, this commit will not
cause any behavioral change to the exclusive groups.
Link: https://lkml.kernel.org/r/20220228081314.5770-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20220228081314.5770-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <skhan@linuxfoundation.org>
Cc: David Rientjes <rientjes@google.com>
Cc: Xin Hao <xhao@linux.alibaba.com>
Cc: Greg Kroah-Hartman <gregkh@linuxfoundation.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-22 21:49:21 +00:00
|
|
|
break;
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
}
|
|
|
|
return err;
|
|
|
|
}
|
|
|
|
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
/*
|
|
|
|
* Reset the aggregated monitoring results ('nr_accesses' of each region).
|
|
|
|
*/
|
|
|
|
static void kdamond_reset_aggregated(struct damon_ctx *c)
|
|
|
|
{
|
|
|
|
struct damon_target *t;
|
2022-01-14 22:10:50 +00:00
|
|
|
unsigned int ti = 0; /* target's index */
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
|
|
|
|
damon_for_each_target(t, c) {
|
|
|
|
struct damon_region *r;
|
|
|
|
|
2021-09-08 02:56:48 +00:00
|
|
|
damon_for_each_region(r, t) {
|
2023-09-07 02:29:29 +00:00
|
|
|
trace_damon_aggregated(ti, r, damon_nr_regions(t));
|
mm/damon/core: account age of target regions
Patch series "Implement Data Access Monitoring-based Memory Operation Schemes".
Introduction
============
DAMON[1] can be used as a primitive for data access aware memory
management optimizations. For that, users who want such optimizations
should run DAMON, read the monitoring results, analyze it, plan a new
memory management scheme, and apply the new scheme by themselves. Such
efforts will be inevitable for some complicated optimizations.
However, in many other cases, the users would simply want the system to
apply a memory management action to a memory region of a specific size
having a specific access frequency for a specific time. For example,
"page out a memory region larger than 100 MiB keeping only rare accesses
more than 2 minutes", or "Do not use THP for a memory region larger than
2 MiB rarely accessed for more than 1 seconds".
To make the works easier and non-redundant, this patchset implements a
new feature of DAMON, which is called Data Access Monitoring-based
Operation Schemes (DAMOS). Using the feature, users can describe the
normal schemes in a simple way and ask DAMON to execute those on its
own.
[1] https://damonitor.github.io
Evaluations
===========
DAMOS is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation
are not for production but only for proof of concepts.
Please refer to the showcase web site's evaluation document[1] for
detailed evaluation setup and results.
[1] https://damonitor.github.io/doc/html/v34/vm/damon/eval.html
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are
another couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://git.kernel.org/sj/h/damon/for-v5.4.y
- For v5.10.y: https://git.kernel.org/sj/h/damon/for-v5.10.y
Sequence Of Patches
===================
The 1st patch accounts age of each region. The 2nd patch implements the
core of the DAMON-based operation schemes feature. The 3rd patch makes
the default monitoring primitives for virtual address spaces to support
the schemes. From this point, the kernel space users can use DAMOS.
The 4th patch exports the feature to the user space via the debugfs
interface. The 5th patch implements schemes statistics feature for
easier tuning of the schemes and runtime access pattern analysis, and
the 6th patch adds selftests for these changes. Finally, the 7th patch
documents this new feature.
This patch (of 7):
DAMON can be used for data access pattern aware memory management
optimizations. For that, users should run DAMON, read the monitoring
results, analyze it, plan a new memory management scheme, and apply the
new scheme by themselves. It would not be too hard, but still require
some level of effort. For complicated cases, this effort is inevitable.
That said, in many cases, users would simply want to apply an actions to
a memory region of a specific size having a specific access frequency
for a specific time. For example, "page out a memory region larger than
100 MiB but having a low access frequency more than 10 minutes", or "Use
THP for a memory region larger than 2 MiB having a high access frequency
for more than 2 seconds".
For such optimizations, users will need to first account the age of each
region themselves. To reduce such efforts, this implements a simple age
account of each region in DAMON. For each aggregation step, DAMON
compares the access frequency with that from last aggregation and reset
the age of the region if the change is significant. Else, the age is
incremented. Also, in case of the merge of regions, the region
size-weighted average of the ages is set as the age of merged new
region.
Link: https://lkml.kernel.org/r/20211001125604.29660-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20211001125604.29660-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Greg Thelen <gthelen@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: David Rienjes <rientjes@google.com>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:18 +00:00
|
|
|
r->last_nr_accesses = r->nr_accesses;
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
r->nr_accesses = 0;
|
2021-09-08 02:56:48 +00:00
|
|
|
}
|
2022-01-14 22:10:50 +00:00
|
|
|
ti++;
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
}
|
|
|
|
}
|
|
|
|
|
2022-08-13 15:19:03 +00:00
|
|
|
static void damon_split_region_at(struct damon_target *t,
|
|
|
|
struct damon_region *r, unsigned long sz_r);
|
2021-11-05 20:47:16 +00:00
|
|
|
|
2021-11-05 20:47:33 +00:00
|
|
|
static bool __damos_valid_target(struct damon_region *r, struct damos *s)
|
|
|
|
{
|
|
|
|
unsigned long sz;
|
mm/damon/core: make DAMOS uses nr_accesses_bp instead of nr_accesses
Patch series "mm/damon: implement DAMOS apply intervals".
DAMON-based operation schemes are applied for every aggregation interval.
That is mainly because schemes are using nr_accesses, which be complete to
be used for every aggregation interval.
This makes some DAMOS use cases be tricky. Quota setting under long
aggregation interval is one such example. Suppose the aggregation
interval is ten seconds, and there is a scheme having CPU quota 100ms per
1s. The scheme will actually uses 100ms per ten seconds, since it cannobe
be applied before next aggregation interval. The feature is working as
intended, but the results might not that intuitive for some users. This
could be fixed by updating the quota to 1s per 10s. But, in the case, the
CPU usage of DAMOS could look like spikes, and actually make a bad effect
to other CPU-sensitive workloads.
Also, with such huge aggregation interval, users may want schemes to be
applied more frequently.
DAMON provides nr_accesses_bp, which is updated for each sampling interval
in a way that reasonable to be used. By using that instead of
nr_accesses, DAMOS can have its own time interval and mitigate abovely
mentioned issues.
This patchset makes DAMOS schemes to use nr_accesses_bp instead of
nr_accesses, and have their own timing intervals. Also update DAMOS tried
regions sysfs files and DAMOS before_apply tracepoint to use the new data
as their source. Note that the interval is zero by default, and it is
interpreted to use the aggregation interval instead. This avoids making
user-visible behavioral changes.
Patches Seuqeunce
-----------------
The first patch (patch 1/9) makes DAMOS uses nr_accesses_bp instead of
nr_accesses, and following two patches (patches 2/9 and 3/9) updates DAMON
sysfs interface for DAMOS tried regions and the DAMOS before_apply
tracespoint to use nr_accesses_bp instead of nr_accesses, respectively.
The following two patches (patches 4/9 and 5/9) implements the
scheme-specific apply interval for DAMON kernel API users and update the
design document for the new feature.
Finally, the following four patches (patches 6/9, 7/9, 8/9 and 9/9) add
support of the feature in DAMON sysfs interface, add a simple selftest
test case, and document the new file on the usage and the ABI documents,
repsectively.
This patch (of 9):
DAMON provides nr_accesses_bp, which becomes same to nr_accesses * 10000
for every aggregation interval, but updated every sampling interval with a
reasonable accuracy. Since DAMON-based operation schemes are applied in
every aggregation interval using nr_accesses, using nr_accesses_bp instead
will make no difference to users. Meanwhile, it allows DAMOS to apply the
schemes in a time interval that less than the aggregation interval. It
could be useful and more flexible for some cases. Do it.
Link: https://lkml.kernel.org/r/20230916020945.47296-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20230916020945.47296-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (Google) <rostedt@goodmis.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-16 02:09:37 +00:00
|
|
|
unsigned int nr_accesses = r->nr_accesses_bp / 10000;
|
2021-11-05 20:47:33 +00:00
|
|
|
|
2022-09-27 00:19:46 +00:00
|
|
|
sz = damon_sz_region(r);
|
2022-09-08 19:14:43 +00:00
|
|
|
return s->pattern.min_sz_region <= sz &&
|
|
|
|
sz <= s->pattern.max_sz_region &&
|
mm/damon/core: make DAMOS uses nr_accesses_bp instead of nr_accesses
Patch series "mm/damon: implement DAMOS apply intervals".
DAMON-based operation schemes are applied for every aggregation interval.
That is mainly because schemes are using nr_accesses, which be complete to
be used for every aggregation interval.
This makes some DAMOS use cases be tricky. Quota setting under long
aggregation interval is one such example. Suppose the aggregation
interval is ten seconds, and there is a scheme having CPU quota 100ms per
1s. The scheme will actually uses 100ms per ten seconds, since it cannobe
be applied before next aggregation interval. The feature is working as
intended, but the results might not that intuitive for some users. This
could be fixed by updating the quota to 1s per 10s. But, in the case, the
CPU usage of DAMOS could look like spikes, and actually make a bad effect
to other CPU-sensitive workloads.
Also, with such huge aggregation interval, users may want schemes to be
applied more frequently.
DAMON provides nr_accesses_bp, which is updated for each sampling interval
in a way that reasonable to be used. By using that instead of
nr_accesses, DAMOS can have its own time interval and mitigate abovely
mentioned issues.
This patchset makes DAMOS schemes to use nr_accesses_bp instead of
nr_accesses, and have their own timing intervals. Also update DAMOS tried
regions sysfs files and DAMOS before_apply tracepoint to use the new data
as their source. Note that the interval is zero by default, and it is
interpreted to use the aggregation interval instead. This avoids making
user-visible behavioral changes.
Patches Seuqeunce
-----------------
The first patch (patch 1/9) makes DAMOS uses nr_accesses_bp instead of
nr_accesses, and following two patches (patches 2/9 and 3/9) updates DAMON
sysfs interface for DAMOS tried regions and the DAMOS before_apply
tracespoint to use nr_accesses_bp instead of nr_accesses, respectively.
The following two patches (patches 4/9 and 5/9) implements the
scheme-specific apply interval for DAMON kernel API users and update the
design document for the new feature.
Finally, the following four patches (patches 6/9, 7/9, 8/9 and 9/9) add
support of the feature in DAMON sysfs interface, add a simple selftest
test case, and document the new file on the usage and the ABI documents,
repsectively.
This patch (of 9):
DAMON provides nr_accesses_bp, which becomes same to nr_accesses * 10000
for every aggregation interval, but updated every sampling interval with a
reasonable accuracy. Since DAMON-based operation schemes are applied in
every aggregation interval using nr_accesses, using nr_accesses_bp instead
will make no difference to users. Meanwhile, it allows DAMOS to apply the
schemes in a time interval that less than the aggregation interval. It
could be useful and more flexible for some cases. Do it.
Link: https://lkml.kernel.org/r/20230916020945.47296-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20230916020945.47296-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (Google) <rostedt@goodmis.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-16 02:09:37 +00:00
|
|
|
s->pattern.min_nr_accesses <= nr_accesses &&
|
|
|
|
nr_accesses <= s->pattern.max_nr_accesses &&
|
2022-09-08 19:14:43 +00:00
|
|
|
s->pattern.min_age_region <= r->age &&
|
|
|
|
r->age <= s->pattern.max_age_region;
|
2021-11-05 20:47:33 +00:00
|
|
|
}
|
|
|
|
|
|
|
|
static bool damos_valid_target(struct damon_ctx *c, struct damon_target *t,
|
|
|
|
struct damon_region *r, struct damos *s)
|
|
|
|
{
|
|
|
|
bool ret = __damos_valid_target(r, s);
|
|
|
|
|
2022-03-22 21:48:46 +00:00
|
|
|
if (!ret || !s->quota.esz || !c->ops.get_scheme_score)
|
2021-11-05 20:47:33 +00:00
|
|
|
return ret;
|
|
|
|
|
2022-03-22 21:48:46 +00:00
|
|
|
return c->ops.get_scheme_score(c, t, r, s) >= s->quota.min_score;
|
2021-11-05 20:47:33 +00:00
|
|
|
}
|
|
|
|
|
mm/damon/core: split out DAMOS-charged region skip logic into a new function
Patch series "mm/damon: cleanup and refactoring code", v2.
This patchset cleans up and refactors a range of DAMON code including the
core, DAMON sysfs interface, and DAMON modules, for better readability and
convenient future feature implementations.
In detail, this patchset splits unnecessarily long and complex functions
in core into smaller functions (patches 1-4). Then, it cleans up the
DAMON sysfs interface by using more type-safe code (patch 5) and removing
unnecessary function parameters (patch 6). Further, it refactor the code
by distributing the code into multiple files (patches 7-10). Last two
patches (patches 11 and 12) deduplicates and remove unnecessary header
inclusion in DAMON modules (reclaim and lru_sort).
This patch (of 12):
The DAMOS action applying function, 'damon_do_apply_schemes()', is quite
long and not so simple. Split out the already quota-charged region skip
code, which is not a small amount of simple code, into a new function with
some comments for better readability.
Link: https://lkml.kernel.org/r/20221026225943.100429-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20221026225943.100429-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2022-10-26 22:59:32 +00:00
|
|
|
/*
|
|
|
|
* damos_skip_charged_region() - Check if the given region or starting part of
|
|
|
|
* it is already charged for the DAMOS quota.
|
|
|
|
* @t: The target of the region.
|
|
|
|
* @rp: The pointer to the region.
|
|
|
|
* @s: The scheme to be applied.
|
|
|
|
*
|
|
|
|
* If a quota of a scheme has exceeded in a quota charge window, the scheme's
|
|
|
|
* action would applied to only a part of the target access pattern fulfilling
|
|
|
|
* regions. To avoid applying the scheme action to only already applied
|
|
|
|
* regions, DAMON skips applying the scheme action to the regions that charged
|
|
|
|
* in the previous charge window.
|
|
|
|
*
|
|
|
|
* This function checks if a given region should be skipped or not for the
|
|
|
|
* reason. If only the starting part of the region has previously charged,
|
|
|
|
* this function splits the region into two so that the second one covers the
|
|
|
|
* area that not charged in the previous charge widnow and saves the second
|
|
|
|
* region in *rp and returns false, so that the caller can apply DAMON action
|
|
|
|
* to the second one.
|
|
|
|
*
|
|
|
|
* Return: true if the region should be entirely skipped, false otherwise.
|
|
|
|
*/
|
|
|
|
static bool damos_skip_charged_region(struct damon_target *t,
|
|
|
|
struct damon_region **rp, struct damos *s)
|
|
|
|
{
|
|
|
|
struct damon_region *r = *rp;
|
|
|
|
struct damos_quota *quota = &s->quota;
|
|
|
|
unsigned long sz_to_skip;
|
|
|
|
|
|
|
|
/* Skip previously charged regions */
|
|
|
|
if (quota->charge_target_from) {
|
|
|
|
if (t != quota->charge_target_from)
|
|
|
|
return true;
|
|
|
|
if (r == damon_last_region(t)) {
|
|
|
|
quota->charge_target_from = NULL;
|
|
|
|
quota->charge_addr_from = 0;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
if (quota->charge_addr_from &&
|
|
|
|
r->ar.end <= quota->charge_addr_from)
|
|
|
|
return true;
|
|
|
|
|
|
|
|
if (quota->charge_addr_from && r->ar.start <
|
|
|
|
quota->charge_addr_from) {
|
|
|
|
sz_to_skip = ALIGN_DOWN(quota->charge_addr_from -
|
|
|
|
r->ar.start, DAMON_MIN_REGION);
|
|
|
|
if (!sz_to_skip) {
|
|
|
|
if (damon_sz_region(r) <= DAMON_MIN_REGION)
|
|
|
|
return true;
|
|
|
|
sz_to_skip = DAMON_MIN_REGION;
|
|
|
|
}
|
|
|
|
damon_split_region_at(t, r, sz_to_skip);
|
|
|
|
r = damon_next_region(r);
|
|
|
|
*rp = r;
|
|
|
|
}
|
|
|
|
quota->charge_target_from = NULL;
|
|
|
|
quota->charge_addr_from = 0;
|
|
|
|
}
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
|
2022-10-26 22:59:34 +00:00
|
|
|
static void damos_update_stat(struct damos *s,
|
|
|
|
unsigned long sz_tried, unsigned long sz_applied)
|
|
|
|
{
|
|
|
|
s->stat.nr_tried++;
|
|
|
|
s->stat.sz_tried += sz_tried;
|
|
|
|
if (sz_applied)
|
|
|
|
s->stat.nr_applied++;
|
|
|
|
s->stat.sz_applied += sz_applied;
|
|
|
|
}
|
|
|
|
|
2023-08-02 21:43:07 +00:00
|
|
|
static bool __damos_filter_out(struct damon_ctx *ctx, struct damon_target *t,
|
|
|
|
struct damon_region *r, struct damos_filter *filter)
|
mm/damon/core: introduce address range type damos filter
Patch series "Extend DAMOS filters for address ranges and DAMON monitoring
targets"
There are use cases that need to apply DAMOS schemes to specific address
ranges or DAMON monitoring targets. NUMA nodes in the physical address
space, special memory objects in the virtual address space, and monitoring
target specific efficient monitoring results snapshot retrieval could be
examples of such use cases. This patchset extends DAMOS filters feature
for such cases, by implementing two more filter types, namely address
ranges and DAMON monitoring types.
Patches sequence
----------------
The first seven patches are for the address ranges based DAMOS filter.
The first patch implements the filter feature and expose it via DAMON
kernel API. The second patch further expose the feature to users via
DAMON sysfs interface. The third and fourth patches implement unit tests
and selftests for the feature. Three patches (fifth to seventh) updating
the documents follow.
The following six patches are for the DAMON monitoring target based DAMOS
filter. The eighth patch implements the feature in the core layer and
expose it via DAMON's kernel API. The ninth patch further expose it to
users via DAMON sysfs interface. Tenth patch add a selftest, and two
patches (eleventh and twelfth) update documents.
[1] https://lore.kernel.org/damon/20230728203444.70703-1-sj@kernel.org/
This patch (of 13):
Users can know special characteristic of specific address ranges. NUMA
nodes or special objects or buffers in virtual address space could be such
examples. For such cases, DAMOS schemes could required to be applied to
only specific address ranges. Implement yet another type of DAMOS filter
for the purpose.
Note that the existing filter types, namely anon pages and memcg DAMOS
filters needed page level type check. Because such check can be done
efficiently in the opertions set layer, those filters are handled in
operations set layer. Specifically, only paddr operations set
implementation supports these filters. Also, because statistics counting
is done in the DAMON core layer, the regions that filtered out by these
filters are counted as tried but failed to the statistics.
Unlike those, address range based filters can efficiently handled in the
core layer. Hence, do the handling in the layer, and count the regions
that filtered out by those as the scheme has not tried for the region.
This difference should clearly documented.
Link: https://lkml.kernel.org/r/20230802214312.110532-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20230802214312.110532-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-08-02 21:43:00 +00:00
|
|
|
{
|
|
|
|
bool matched = false;
|
2023-08-02 21:43:07 +00:00
|
|
|
struct damon_target *ti;
|
|
|
|
int target_idx = 0;
|
mm/damon/core: introduce address range type damos filter
Patch series "Extend DAMOS filters for address ranges and DAMON monitoring
targets"
There are use cases that need to apply DAMOS schemes to specific address
ranges or DAMON monitoring targets. NUMA nodes in the physical address
space, special memory objects in the virtual address space, and monitoring
target specific efficient monitoring results snapshot retrieval could be
examples of such use cases. This patchset extends DAMOS filters feature
for such cases, by implementing two more filter types, namely address
ranges and DAMON monitoring types.
Patches sequence
----------------
The first seven patches are for the address ranges based DAMOS filter.
The first patch implements the filter feature and expose it via DAMON
kernel API. The second patch further expose the feature to users via
DAMON sysfs interface. The third and fourth patches implement unit tests
and selftests for the feature. Three patches (fifth to seventh) updating
the documents follow.
The following six patches are for the DAMON monitoring target based DAMOS
filter. The eighth patch implements the feature in the core layer and
expose it via DAMON's kernel API. The ninth patch further expose it to
users via DAMON sysfs interface. Tenth patch add a selftest, and two
patches (eleventh and twelfth) update documents.
[1] https://lore.kernel.org/damon/20230728203444.70703-1-sj@kernel.org/
This patch (of 13):
Users can know special characteristic of specific address ranges. NUMA
nodes or special objects or buffers in virtual address space could be such
examples. For such cases, DAMOS schemes could required to be applied to
only specific address ranges. Implement yet another type of DAMOS filter
for the purpose.
Note that the existing filter types, namely anon pages and memcg DAMOS
filters needed page level type check. Because such check can be done
efficiently in the opertions set layer, those filters are handled in
operations set layer. Specifically, only paddr operations set
implementation supports these filters. Also, because statistics counting
is done in the DAMON core layer, the regions that filtered out by these
filters are counted as tried but failed to the statistics.
Unlike those, address range based filters can efficiently handled in the
core layer. Hence, do the handling in the layer, and count the regions
that filtered out by those as the scheme has not tried for the region.
This difference should clearly documented.
Link: https://lkml.kernel.org/r/20230802214312.110532-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20230802214312.110532-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-08-02 21:43:00 +00:00
|
|
|
unsigned long start, end;
|
|
|
|
|
|
|
|
switch (filter->type) {
|
2023-08-02 21:43:07 +00:00
|
|
|
case DAMOS_FILTER_TYPE_TARGET:
|
|
|
|
damon_for_each_target(ti, ctx) {
|
|
|
|
if (ti == t)
|
|
|
|
break;
|
|
|
|
target_idx++;
|
|
|
|
}
|
|
|
|
matched = target_idx == filter->target_idx;
|
|
|
|
break;
|
mm/damon/core: introduce address range type damos filter
Patch series "Extend DAMOS filters for address ranges and DAMON monitoring
targets"
There are use cases that need to apply DAMOS schemes to specific address
ranges or DAMON monitoring targets. NUMA nodes in the physical address
space, special memory objects in the virtual address space, and monitoring
target specific efficient monitoring results snapshot retrieval could be
examples of such use cases. This patchset extends DAMOS filters feature
for such cases, by implementing two more filter types, namely address
ranges and DAMON monitoring types.
Patches sequence
----------------
The first seven patches are for the address ranges based DAMOS filter.
The first patch implements the filter feature and expose it via DAMON
kernel API. The second patch further expose the feature to users via
DAMON sysfs interface. The third and fourth patches implement unit tests
and selftests for the feature. Three patches (fifth to seventh) updating
the documents follow.
The following six patches are for the DAMON monitoring target based DAMOS
filter. The eighth patch implements the feature in the core layer and
expose it via DAMON's kernel API. The ninth patch further expose it to
users via DAMON sysfs interface. Tenth patch add a selftest, and two
patches (eleventh and twelfth) update documents.
[1] https://lore.kernel.org/damon/20230728203444.70703-1-sj@kernel.org/
This patch (of 13):
Users can know special characteristic of specific address ranges. NUMA
nodes or special objects or buffers in virtual address space could be such
examples. For such cases, DAMOS schemes could required to be applied to
only specific address ranges. Implement yet another type of DAMOS filter
for the purpose.
Note that the existing filter types, namely anon pages and memcg DAMOS
filters needed page level type check. Because such check can be done
efficiently in the opertions set layer, those filters are handled in
operations set layer. Specifically, only paddr operations set
implementation supports these filters. Also, because statistics counting
is done in the DAMON core layer, the regions that filtered out by these
filters are counted as tried but failed to the statistics.
Unlike those, address range based filters can efficiently handled in the
core layer. Hence, do the handling in the layer, and count the regions
that filtered out by those as the scheme has not tried for the region.
This difference should clearly documented.
Link: https://lkml.kernel.org/r/20230802214312.110532-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20230802214312.110532-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-08-02 21:43:00 +00:00
|
|
|
case DAMOS_FILTER_TYPE_ADDR:
|
|
|
|
start = ALIGN_DOWN(filter->addr_range.start, DAMON_MIN_REGION);
|
|
|
|
end = ALIGN_DOWN(filter->addr_range.end, DAMON_MIN_REGION);
|
|
|
|
|
|
|
|
/* inside the range */
|
|
|
|
if (start <= r->ar.start && r->ar.end <= end) {
|
|
|
|
matched = true;
|
|
|
|
break;
|
|
|
|
}
|
|
|
|
/* outside of the range */
|
|
|
|
if (r->ar.end <= start || end <= r->ar.start) {
|
|
|
|
matched = false;
|
|
|
|
break;
|
|
|
|
}
|
|
|
|
/* start before the range and overlap */
|
|
|
|
if (r->ar.start < start) {
|
|
|
|
damon_split_region_at(t, r, start - r->ar.start);
|
|
|
|
matched = false;
|
|
|
|
break;
|
|
|
|
}
|
|
|
|
/* start inside the range */
|
|
|
|
damon_split_region_at(t, r, end - r->ar.start);
|
|
|
|
matched = true;
|
|
|
|
break;
|
|
|
|
default:
|
|
|
|
break;
|
|
|
|
}
|
|
|
|
|
|
|
|
return matched == filter->matching;
|
|
|
|
}
|
|
|
|
|
2023-08-02 21:43:07 +00:00
|
|
|
static bool damos_filter_out(struct damon_ctx *ctx, struct damon_target *t,
|
|
|
|
struct damon_region *r, struct damos *s)
|
mm/damon/core: introduce address range type damos filter
Patch series "Extend DAMOS filters for address ranges and DAMON monitoring
targets"
There are use cases that need to apply DAMOS schemes to specific address
ranges or DAMON monitoring targets. NUMA nodes in the physical address
space, special memory objects in the virtual address space, and monitoring
target specific efficient monitoring results snapshot retrieval could be
examples of such use cases. This patchset extends DAMOS filters feature
for such cases, by implementing two more filter types, namely address
ranges and DAMON monitoring types.
Patches sequence
----------------
The first seven patches are for the address ranges based DAMOS filter.
The first patch implements the filter feature and expose it via DAMON
kernel API. The second patch further expose the feature to users via
DAMON sysfs interface. The third and fourth patches implement unit tests
and selftests for the feature. Three patches (fifth to seventh) updating
the documents follow.
The following six patches are for the DAMON monitoring target based DAMOS
filter. The eighth patch implements the feature in the core layer and
expose it via DAMON's kernel API. The ninth patch further expose it to
users via DAMON sysfs interface. Tenth patch add a selftest, and two
patches (eleventh and twelfth) update documents.
[1] https://lore.kernel.org/damon/20230728203444.70703-1-sj@kernel.org/
This patch (of 13):
Users can know special characteristic of specific address ranges. NUMA
nodes or special objects or buffers in virtual address space could be such
examples. For such cases, DAMOS schemes could required to be applied to
only specific address ranges. Implement yet another type of DAMOS filter
for the purpose.
Note that the existing filter types, namely anon pages and memcg DAMOS
filters needed page level type check. Because such check can be done
efficiently in the opertions set layer, those filters are handled in
operations set layer. Specifically, only paddr operations set
implementation supports these filters. Also, because statistics counting
is done in the DAMON core layer, the regions that filtered out by these
filters are counted as tried but failed to the statistics.
Unlike those, address range based filters can efficiently handled in the
core layer. Hence, do the handling in the layer, and count the regions
that filtered out by those as the scheme has not tried for the region.
This difference should clearly documented.
Link: https://lkml.kernel.org/r/20230802214312.110532-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20230802214312.110532-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-08-02 21:43:00 +00:00
|
|
|
{
|
|
|
|
struct damos_filter *filter;
|
|
|
|
|
|
|
|
damos_for_each_filter(filter, s) {
|
2023-08-02 21:43:07 +00:00
|
|
|
if (__damos_filter_out(ctx, t, r, filter))
|
mm/damon/core: introduce address range type damos filter
Patch series "Extend DAMOS filters for address ranges and DAMON monitoring
targets"
There are use cases that need to apply DAMOS schemes to specific address
ranges or DAMON monitoring targets. NUMA nodes in the physical address
space, special memory objects in the virtual address space, and monitoring
target specific efficient monitoring results snapshot retrieval could be
examples of such use cases. This patchset extends DAMOS filters feature
for such cases, by implementing two more filter types, namely address
ranges and DAMON monitoring types.
Patches sequence
----------------
The first seven patches are for the address ranges based DAMOS filter.
The first patch implements the filter feature and expose it via DAMON
kernel API. The second patch further expose the feature to users via
DAMON sysfs interface. The third and fourth patches implement unit tests
and selftests for the feature. Three patches (fifth to seventh) updating
the documents follow.
The following six patches are for the DAMON monitoring target based DAMOS
filter. The eighth patch implements the feature in the core layer and
expose it via DAMON's kernel API. The ninth patch further expose it to
users via DAMON sysfs interface. Tenth patch add a selftest, and two
patches (eleventh and twelfth) update documents.
[1] https://lore.kernel.org/damon/20230728203444.70703-1-sj@kernel.org/
This patch (of 13):
Users can know special characteristic of specific address ranges. NUMA
nodes or special objects or buffers in virtual address space could be such
examples. For such cases, DAMOS schemes could required to be applied to
only specific address ranges. Implement yet another type of DAMOS filter
for the purpose.
Note that the existing filter types, namely anon pages and memcg DAMOS
filters needed page level type check. Because such check can be done
efficiently in the opertions set layer, those filters are handled in
operations set layer. Specifically, only paddr operations set
implementation supports these filters. Also, because statistics counting
is done in the DAMON core layer, the regions that filtered out by these
filters are counted as tried but failed to the statistics.
Unlike those, address range based filters can efficiently handled in the
core layer. Hence, do the handling in the layer, and count the regions
that filtered out by those as the scheme has not tried for the region.
This difference should clearly documented.
Link: https://lkml.kernel.org/r/20230802214312.110532-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20230802214312.110532-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-08-02 21:43:00 +00:00
|
|
|
return true;
|
|
|
|
}
|
|
|
|
return false;
|
|
|
|
}
|
|
|
|
|
2022-10-26 22:59:33 +00:00
|
|
|
static void damos_apply_scheme(struct damon_ctx *c, struct damon_target *t,
|
|
|
|
struct damon_region *r, struct damos *s)
|
|
|
|
{
|
|
|
|
struct damos_quota *quota = &s->quota;
|
|
|
|
unsigned long sz = damon_sz_region(r);
|
|
|
|
struct timespec64 begin, end;
|
|
|
|
unsigned long sz_applied = 0;
|
mm/damon/core: add a callback for scheme target regions check
Patch series "efficiently expose damos action tried regions information".
DAMON users can retrieve the monitoring results via 'after_aggregation'
callbacks if the user is using the kernel API, or 'damon_aggregated'
tracepoint if the user is in the user space. Those are useful if full
monitoring results are necessary. However, if the user has interest in
only a snapshot of the results for some regions having specific access
pattern, the interfaces could be inefficient. For example, some users
only want to know which memory regions are not accessed for more than a
specific time at the moment.
Also, some DAMOS users would want to know exactly to what memory regions
the schemes' actions tried to be applied, for a debugging or a tuning. As
DAMOS has its internal mechanism for quota and regions prioritization, the
users would need to simulate DAMOS' mechanism against the monitoring
results. That's unnecessarily complex.
This patchset implements DAMON kernel API callbacks and sysfs directory
for efficient exposure of the information for the use cases. The new
callback will be called for each region when a DAMOS action is gonna tried
to be applied to it. The sysfs directory will be called 'tried_regions'
and placed under each scheme sysfs directory. Users can write a special
keyworkd, 'update_schemes_regions', to the 'state' file of a kdamond sysfs
directory. Then, DAMON sysfs interface will fill the directory with the
information of regions that corresponding scheme action was tried to be
applied for next one aggregation interval.
Patches Sequence
----------------
The first one (patch 1) implements the callback for the kernel space
users. Following two patches (patches 2 and 3) implements sysfs
directories for the information and its sub directories. Two patches
(patches 4 and 5) for implementing the special keywords for filling the
data to and cleaning up the directories follow. Patch 6 adds a selftest
for the new sysfs directory. Finally, two patches (patches 7 and 8)
document the new feature in the administrator guide and the ABI document.
This patch (of 8):
Getting DAMON monitoring results of only specific access pattern (e.g.,
getting address ranges of memory that not accessed at all for two minutes)
can be useful for efficient monitoring of the system. The information can
also be helpful for deep level investigation of DAMON-based operation
schemes.
For that, users need to record (in case of the user space users) or
iterate (in case of the kernel space users) full monitoring results and
filter it out for the specific access pattern. In case of the DAMOS
investigation, users will even need to simulate DAMOS' quota and
prioritization mechanisms. It's inefficient and complex.
Add a new DAMON callback that will be called before each scheme is applied
to each region. DAMON kernel API users will be able to do the query-like
monitoring results collection, or DAMOS investigation in an efficient and
simple way using it.
Commits for providing the capability to the user space users will follow.
Link: https://lkml.kernel.org/r/20221101220328.95765-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20221101220328.95765-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2022-11-01 22:03:21 +00:00
|
|
|
int err = 0;
|
mm/damon/core: add a tracepoint for damos apply target regions
Patch series "mm/damon: add a tracepoint for damos apply target regions",
v2.
DAMON provides damon_aggregated tracepoint to let users record full
monitoring results. Sometimes, users need to record monitoring results of
specific pattern. DAMOS tried regions directory of DAMON sysfs interface
allows it, but the interface is mainly designed for snapshots and
therefore would be inefficient for such recording. Implement yet another
tracepoint for efficient support of the usecase.
This patch (of 2):
DAMON provides damon_aggregated tracepoint, which exposes details of each
region and its access monitoring results. It is useful for getting whole
monitoring results, e.g., for recording purposes.
For investigations of DAMOS, DAMON Sysfs interface provides DAMOS
statistics and tried_regions directory. But, those provides only
statistics and snapshots. If the scheme is frequently applied and if the
user needs to know every detail of DAMOS behavior, the snapshot-based
interface could be insufficient and expensive.
As a last resort, userspace users need to record the all monitoring
results via damon_aggregated tracepoint and simulate how DAMOS would
worked. It is unnecessarily complicated. DAMON kernel API users,
meanwhile, can do that easily via before_damos_apply() callback field of
'struct damon_callback', though.
Add a tracepoint that will be called just after before_damos_apply()
callback for more convenient investigations of DAMOS. The tracepoint
exposes all details about each regions, similar to damon_aggregated
tracepoint.
Please note that DAMOS is currently not only for memory management but
also for query-like efficient monitoring results retrievals (when 'stat'
action is used). Until now, only statistics or snapshots were supported.
Addition of this tracepoint allows efficient full recording of DAMOS-based
filtered monitoring results.
Link: https://lkml.kernel.org/r/20230913022050.2109-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20230913022050.2109-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Reviewed-by: Steven Rostedt (Google) <rostedt@goodmis.org> [tracing]
Cc: Jonathan Corbet <corbet@lwn.net>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-13 02:20:49 +00:00
|
|
|
/*
|
|
|
|
* We plan to support multiple context per kdamond, as DAMON sysfs
|
|
|
|
* implies with 'nr_contexts' file. Nevertheless, only single context
|
|
|
|
* per kdamond is supported for now. So, we can simply use '0' context
|
|
|
|
* index here.
|
|
|
|
*/
|
|
|
|
unsigned int cidx = 0;
|
|
|
|
struct damos *siter; /* schemes iterator */
|
|
|
|
unsigned int sidx = 0;
|
|
|
|
struct damon_target *titer; /* targets iterator */
|
|
|
|
unsigned int tidx = 0;
|
|
|
|
bool do_trace = false;
|
|
|
|
|
|
|
|
/* get indices for trace_damos_before_apply() */
|
|
|
|
if (trace_damos_before_apply_enabled()) {
|
|
|
|
damon_for_each_scheme(siter, c) {
|
|
|
|
if (siter == s)
|
|
|
|
break;
|
|
|
|
sidx++;
|
|
|
|
}
|
|
|
|
damon_for_each_target(titer, c) {
|
|
|
|
if (titer == t)
|
|
|
|
break;
|
|
|
|
tidx++;
|
|
|
|
}
|
|
|
|
do_trace = true;
|
|
|
|
}
|
2022-10-26 22:59:33 +00:00
|
|
|
|
|
|
|
if (c->ops.apply_scheme) {
|
|
|
|
if (quota->esz && quota->charged_sz + sz > quota->esz) {
|
|
|
|
sz = ALIGN_DOWN(quota->esz - quota->charged_sz,
|
|
|
|
DAMON_MIN_REGION);
|
|
|
|
if (!sz)
|
|
|
|
goto update_stat;
|
|
|
|
damon_split_region_at(t, r, sz);
|
|
|
|
}
|
2023-08-02 21:43:07 +00:00
|
|
|
if (damos_filter_out(c, t, r, s))
|
mm/damon/core: introduce address range type damos filter
Patch series "Extend DAMOS filters for address ranges and DAMON monitoring
targets"
There are use cases that need to apply DAMOS schemes to specific address
ranges or DAMON monitoring targets. NUMA nodes in the physical address
space, special memory objects in the virtual address space, and monitoring
target specific efficient monitoring results snapshot retrieval could be
examples of such use cases. This patchset extends DAMOS filters feature
for such cases, by implementing two more filter types, namely address
ranges and DAMON monitoring types.
Patches sequence
----------------
The first seven patches are for the address ranges based DAMOS filter.
The first patch implements the filter feature and expose it via DAMON
kernel API. The second patch further expose the feature to users via
DAMON sysfs interface. The third and fourth patches implement unit tests
and selftests for the feature. Three patches (fifth to seventh) updating
the documents follow.
The following six patches are for the DAMON monitoring target based DAMOS
filter. The eighth patch implements the feature in the core layer and
expose it via DAMON's kernel API. The ninth patch further expose it to
users via DAMON sysfs interface. Tenth patch add a selftest, and two
patches (eleventh and twelfth) update documents.
[1] https://lore.kernel.org/damon/20230728203444.70703-1-sj@kernel.org/
This patch (of 13):
Users can know special characteristic of specific address ranges. NUMA
nodes or special objects or buffers in virtual address space could be such
examples. For such cases, DAMOS schemes could required to be applied to
only specific address ranges. Implement yet another type of DAMOS filter
for the purpose.
Note that the existing filter types, namely anon pages and memcg DAMOS
filters needed page level type check. Because such check can be done
efficiently in the opertions set layer, those filters are handled in
operations set layer. Specifically, only paddr operations set
implementation supports these filters. Also, because statistics counting
is done in the DAMON core layer, the regions that filtered out by these
filters are counted as tried but failed to the statistics.
Unlike those, address range based filters can efficiently handled in the
core layer. Hence, do the handling in the layer, and count the regions
that filtered out by those as the scheme has not tried for the region.
This difference should clearly documented.
Link: https://lkml.kernel.org/r/20230802214312.110532-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20230802214312.110532-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-08-02 21:43:00 +00:00
|
|
|
return;
|
2022-10-26 22:59:33 +00:00
|
|
|
ktime_get_coarse_ts64(&begin);
|
mm/damon/core: add a callback for scheme target regions check
Patch series "efficiently expose damos action tried regions information".
DAMON users can retrieve the monitoring results via 'after_aggregation'
callbacks if the user is using the kernel API, or 'damon_aggregated'
tracepoint if the user is in the user space. Those are useful if full
monitoring results are necessary. However, if the user has interest in
only a snapshot of the results for some regions having specific access
pattern, the interfaces could be inefficient. For example, some users
only want to know which memory regions are not accessed for more than a
specific time at the moment.
Also, some DAMOS users would want to know exactly to what memory regions
the schemes' actions tried to be applied, for a debugging or a tuning. As
DAMOS has its internal mechanism for quota and regions prioritization, the
users would need to simulate DAMOS' mechanism against the monitoring
results. That's unnecessarily complex.
This patchset implements DAMON kernel API callbacks and sysfs directory
for efficient exposure of the information for the use cases. The new
callback will be called for each region when a DAMOS action is gonna tried
to be applied to it. The sysfs directory will be called 'tried_regions'
and placed under each scheme sysfs directory. Users can write a special
keyworkd, 'update_schemes_regions', to the 'state' file of a kdamond sysfs
directory. Then, DAMON sysfs interface will fill the directory with the
information of regions that corresponding scheme action was tried to be
applied for next one aggregation interval.
Patches Sequence
----------------
The first one (patch 1) implements the callback for the kernel space
users. Following two patches (patches 2 and 3) implements sysfs
directories for the information and its sub directories. Two patches
(patches 4 and 5) for implementing the special keywords for filling the
data to and cleaning up the directories follow. Patch 6 adds a selftest
for the new sysfs directory. Finally, two patches (patches 7 and 8)
document the new feature in the administrator guide and the ABI document.
This patch (of 8):
Getting DAMON monitoring results of only specific access pattern (e.g.,
getting address ranges of memory that not accessed at all for two minutes)
can be useful for efficient monitoring of the system. The information can
also be helpful for deep level investigation of DAMON-based operation
schemes.
For that, users need to record (in case of the user space users) or
iterate (in case of the kernel space users) full monitoring results and
filter it out for the specific access pattern. In case of the DAMOS
investigation, users will even need to simulate DAMOS' quota and
prioritization mechanisms. It's inefficient and complex.
Add a new DAMON callback that will be called before each scheme is applied
to each region. DAMON kernel API users will be able to do the query-like
monitoring results collection, or DAMOS investigation in an efficient and
simple way using it.
Commits for providing the capability to the user space users will follow.
Link: https://lkml.kernel.org/r/20221101220328.95765-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20221101220328.95765-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2022-11-01 22:03:21 +00:00
|
|
|
if (c->callback.before_damos_apply)
|
|
|
|
err = c->callback.before_damos_apply(c, t, r, s);
|
mm/damon/core: add a tracepoint for damos apply target regions
Patch series "mm/damon: add a tracepoint for damos apply target regions",
v2.
DAMON provides damon_aggregated tracepoint to let users record full
monitoring results. Sometimes, users need to record monitoring results of
specific pattern. DAMOS tried regions directory of DAMON sysfs interface
allows it, but the interface is mainly designed for snapshots and
therefore would be inefficient for such recording. Implement yet another
tracepoint for efficient support of the usecase.
This patch (of 2):
DAMON provides damon_aggregated tracepoint, which exposes details of each
region and its access monitoring results. It is useful for getting whole
monitoring results, e.g., for recording purposes.
For investigations of DAMOS, DAMON Sysfs interface provides DAMOS
statistics and tried_regions directory. But, those provides only
statistics and snapshots. If the scheme is frequently applied and if the
user needs to know every detail of DAMOS behavior, the snapshot-based
interface could be insufficient and expensive.
As a last resort, userspace users need to record the all monitoring
results via damon_aggregated tracepoint and simulate how DAMOS would
worked. It is unnecessarily complicated. DAMON kernel API users,
meanwhile, can do that easily via before_damos_apply() callback field of
'struct damon_callback', though.
Add a tracepoint that will be called just after before_damos_apply()
callback for more convenient investigations of DAMOS. The tracepoint
exposes all details about each regions, similar to damon_aggregated
tracepoint.
Please note that DAMOS is currently not only for memory management but
also for query-like efficient monitoring results retrievals (when 'stat'
action is used). Until now, only statistics or snapshots were supported.
Addition of this tracepoint allows efficient full recording of DAMOS-based
filtered monitoring results.
Link: https://lkml.kernel.org/r/20230913022050.2109-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20230913022050.2109-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Reviewed-by: Steven Rostedt (Google) <rostedt@goodmis.org> [tracing]
Cc: Jonathan Corbet <corbet@lwn.net>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-13 02:20:49 +00:00
|
|
|
if (!err) {
|
|
|
|
trace_damos_before_apply(cidx, sidx, tidx, r,
|
|
|
|
damon_nr_regions(t), do_trace);
|
mm/damon/core: add a callback for scheme target regions check
Patch series "efficiently expose damos action tried regions information".
DAMON users can retrieve the monitoring results via 'after_aggregation'
callbacks if the user is using the kernel API, or 'damon_aggregated'
tracepoint if the user is in the user space. Those are useful if full
monitoring results are necessary. However, if the user has interest in
only a snapshot of the results for some regions having specific access
pattern, the interfaces could be inefficient. For example, some users
only want to know which memory regions are not accessed for more than a
specific time at the moment.
Also, some DAMOS users would want to know exactly to what memory regions
the schemes' actions tried to be applied, for a debugging or a tuning. As
DAMOS has its internal mechanism for quota and regions prioritization, the
users would need to simulate DAMOS' mechanism against the monitoring
results. That's unnecessarily complex.
This patchset implements DAMON kernel API callbacks and sysfs directory
for efficient exposure of the information for the use cases. The new
callback will be called for each region when a DAMOS action is gonna tried
to be applied to it. The sysfs directory will be called 'tried_regions'
and placed under each scheme sysfs directory. Users can write a special
keyworkd, 'update_schemes_regions', to the 'state' file of a kdamond sysfs
directory. Then, DAMON sysfs interface will fill the directory with the
information of regions that corresponding scheme action was tried to be
applied for next one aggregation interval.
Patches Sequence
----------------
The first one (patch 1) implements the callback for the kernel space
users. Following two patches (patches 2 and 3) implements sysfs
directories for the information and its sub directories. Two patches
(patches 4 and 5) for implementing the special keywords for filling the
data to and cleaning up the directories follow. Patch 6 adds a selftest
for the new sysfs directory. Finally, two patches (patches 7 and 8)
document the new feature in the administrator guide and the ABI document.
This patch (of 8):
Getting DAMON monitoring results of only specific access pattern (e.g.,
getting address ranges of memory that not accessed at all for two minutes)
can be useful for efficient monitoring of the system. The information can
also be helpful for deep level investigation of DAMON-based operation
schemes.
For that, users need to record (in case of the user space users) or
iterate (in case of the kernel space users) full monitoring results and
filter it out for the specific access pattern. In case of the DAMOS
investigation, users will even need to simulate DAMOS' quota and
prioritization mechanisms. It's inefficient and complex.
Add a new DAMON callback that will be called before each scheme is applied
to each region. DAMON kernel API users will be able to do the query-like
monitoring results collection, or DAMOS investigation in an efficient and
simple way using it.
Commits for providing the capability to the user space users will follow.
Link: https://lkml.kernel.org/r/20221101220328.95765-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20221101220328.95765-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2022-11-01 22:03:21 +00:00
|
|
|
sz_applied = c->ops.apply_scheme(c, t, r, s);
|
mm/damon/core: add a tracepoint for damos apply target regions
Patch series "mm/damon: add a tracepoint for damos apply target regions",
v2.
DAMON provides damon_aggregated tracepoint to let users record full
monitoring results. Sometimes, users need to record monitoring results of
specific pattern. DAMOS tried regions directory of DAMON sysfs interface
allows it, but the interface is mainly designed for snapshots and
therefore would be inefficient for such recording. Implement yet another
tracepoint for efficient support of the usecase.
This patch (of 2):
DAMON provides damon_aggregated tracepoint, which exposes details of each
region and its access monitoring results. It is useful for getting whole
monitoring results, e.g., for recording purposes.
For investigations of DAMOS, DAMON Sysfs interface provides DAMOS
statistics and tried_regions directory. But, those provides only
statistics and snapshots. If the scheme is frequently applied and if the
user needs to know every detail of DAMOS behavior, the snapshot-based
interface could be insufficient and expensive.
As a last resort, userspace users need to record the all monitoring
results via damon_aggregated tracepoint and simulate how DAMOS would
worked. It is unnecessarily complicated. DAMON kernel API users,
meanwhile, can do that easily via before_damos_apply() callback field of
'struct damon_callback', though.
Add a tracepoint that will be called just after before_damos_apply()
callback for more convenient investigations of DAMOS. The tracepoint
exposes all details about each regions, similar to damon_aggregated
tracepoint.
Please note that DAMOS is currently not only for memory management but
also for query-like efficient monitoring results retrievals (when 'stat'
action is used). Until now, only statistics or snapshots were supported.
Addition of this tracepoint allows efficient full recording of DAMOS-based
filtered monitoring results.
Link: https://lkml.kernel.org/r/20230913022050.2109-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20230913022050.2109-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Reviewed-by: Steven Rostedt (Google) <rostedt@goodmis.org> [tracing]
Cc: Jonathan Corbet <corbet@lwn.net>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-13 02:20:49 +00:00
|
|
|
}
|
2022-10-26 22:59:33 +00:00
|
|
|
ktime_get_coarse_ts64(&end);
|
|
|
|
quota->total_charged_ns += timespec64_to_ns(&end) -
|
|
|
|
timespec64_to_ns(&begin);
|
|
|
|
quota->charged_sz += sz;
|
|
|
|
if (quota->esz && quota->charged_sz >= quota->esz) {
|
|
|
|
quota->charge_target_from = t;
|
|
|
|
quota->charge_addr_from = r->ar.end + 1;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
if (s->action != DAMOS_STAT)
|
|
|
|
r->age = 0;
|
|
|
|
|
|
|
|
update_stat:
|
2022-10-26 22:59:34 +00:00
|
|
|
damos_update_stat(s, sz, sz_applied);
|
2022-10-26 22:59:33 +00:00
|
|
|
}
|
|
|
|
|
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS)
In many cases, users might use DAMON for simple data access aware memory
management optimizations such as applying an operation scheme to a
memory region of a specific size having a specific access frequency for
a specific time. For example, "page out a memory region larger than 100
MiB but having a low access frequency more than 10 minutes", or "Use THP
for a memory region larger than 2 MiB having a high access frequency for
more than 2 seconds".
Most simple form of the solution would be doing offline data access
pattern profiling using DAMON and modifying the application source code
or system configuration based on the profiling results. Or, developing
a daemon constructed with two modules (one for access monitoring and the
other for applying memory management actions via mlock(), madvise(),
sysctl, etc) is imaginable.
To avoid users spending their time for implementation of such simple
data access monitoring-based operation schemes, this makes DAMON to
handle such schemes directly. With this change, users can simply
specify their desired schemes to DAMON. Then, DAMON will automatically
apply the schemes to the user-specified target processes.
Each of the schemes is composed with conditions for filtering of the
target memory regions and desired memory management action for the
target. Specifically, the format is::
<min/max size> <min/max access frequency> <min/max age> <action>
The filtering conditions are size of memory region, number of accesses
to the region monitored by DAMON, and the age of the region. The age of
region is incremented periodically but reset when its addresses or
access frequency has significantly changed or the action of a scheme was
applied. For the action, current implementation supports a few of
madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``,
and ``NOHUGEPAGE``.
Because DAMON supports various address spaces and application of the
actions to a monitoring target region is dependent to the type of the
target address space, the application code should be implemented by each
primitives and registered to the framework. Note that this only
implements the framework part. Following commit will implement the
action applications for virtual address spaces primitives.
Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rienjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:22 +00:00
|
|
|
static void damon_do_apply_schemes(struct damon_ctx *c,
|
|
|
|
struct damon_target *t,
|
|
|
|
struct damon_region *r)
|
|
|
|
{
|
|
|
|
struct damos *s;
|
|
|
|
|
|
|
|
damon_for_each_scheme(s, c) {
|
2021-11-05 20:47:16 +00:00
|
|
|
struct damos_quota *quota = &s->quota;
|
|
|
|
|
mm/damon/schemes: activate schemes based on a watermarks mechanism
DAMON-based operation schemes need to be manually turned on and off. In
some use cases, however, the condition for turning a scheme on and off
would depend on the system's situation. For example, schemes for
proactive pages reclamation would need to be turned on when some memory
pressure is detected, and turned off when the system has enough free
memory.
For easier control of schemes activation based on the system situation,
this introduces a watermarks-based mechanism. The client can describe
the watermark metric (e.g., amount of free memory in the system),
watermark check interval, and three watermarks, namely high, mid, and
low. If the scheme is deactivated, it only gets the metric and compare
that to the three watermarks for every check interval. If the metric is
higher than the high watermark, the scheme is deactivated. If the
metric is between the mid watermark and the low watermark, the scheme is
activated. If the metric is lower than the low watermark, the scheme is
deactivated again. This is to allow users fall back to traditional
page-granularity mechanisms.
Link: https://lkml.kernel.org/r/20211019150731.16699-12-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:47:47 +00:00
|
|
|
if (!s->wmarks.activated)
|
|
|
|
continue;
|
|
|
|
|
2021-11-05 20:47:16 +00:00
|
|
|
/* Check the quota */
|
2021-11-05 20:47:23 +00:00
|
|
|
if (quota->esz && quota->charged_sz >= quota->esz)
|
2021-11-05 20:47:16 +00:00
|
|
|
continue;
|
|
|
|
|
mm/damon/core: split out DAMOS-charged region skip logic into a new function
Patch series "mm/damon: cleanup and refactoring code", v2.
This patchset cleans up and refactors a range of DAMON code including the
core, DAMON sysfs interface, and DAMON modules, for better readability and
convenient future feature implementations.
In detail, this patchset splits unnecessarily long and complex functions
in core into smaller functions (patches 1-4). Then, it cleans up the
DAMON sysfs interface by using more type-safe code (patch 5) and removing
unnecessary function parameters (patch 6). Further, it refactor the code
by distributing the code into multiple files (patches 7-10). Last two
patches (patches 11 and 12) deduplicates and remove unnecessary header
inclusion in DAMON modules (reclaim and lru_sort).
This patch (of 12):
The DAMOS action applying function, 'damon_do_apply_schemes()', is quite
long and not so simple. Split out the already quota-charged region skip
code, which is not a small amount of simple code, into a new function with
some comments for better readability.
Link: https://lkml.kernel.org/r/20221026225943.100429-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20221026225943.100429-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2022-10-26 22:59:32 +00:00
|
|
|
if (damos_skip_charged_region(t, &r, s))
|
|
|
|
continue;
|
2021-11-05 20:47:20 +00:00
|
|
|
|
2021-11-05 20:47:33 +00:00
|
|
|
if (!damos_valid_target(c, t, r, s))
|
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS)
In many cases, users might use DAMON for simple data access aware memory
management optimizations such as applying an operation scheme to a
memory region of a specific size having a specific access frequency for
a specific time. For example, "page out a memory region larger than 100
MiB but having a low access frequency more than 10 minutes", or "Use THP
for a memory region larger than 2 MiB having a high access frequency for
more than 2 seconds".
Most simple form of the solution would be doing offline data access
pattern profiling using DAMON and modifying the application source code
or system configuration based on the profiling results. Or, developing
a daemon constructed with two modules (one for access monitoring and the
other for applying memory management actions via mlock(), madvise(),
sysctl, etc) is imaginable.
To avoid users spending their time for implementation of such simple
data access monitoring-based operation schemes, this makes DAMON to
handle such schemes directly. With this change, users can simply
specify their desired schemes to DAMON. Then, DAMON will automatically
apply the schemes to the user-specified target processes.
Each of the schemes is composed with conditions for filtering of the
target memory regions and desired memory management action for the
target. Specifically, the format is::
<min/max size> <min/max access frequency> <min/max age> <action>
The filtering conditions are size of memory region, number of accesses
to the region monitored by DAMON, and the age of the region. The age of
region is incremented periodically but reset when its addresses or
access frequency has significantly changed or the action of a scheme was
applied. For the action, current implementation supports a few of
madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``,
and ``NOHUGEPAGE``.
Because DAMON supports various address spaces and application of the
actions to a monitoring target region is dependent to the type of the
target address space, the application code should be implemented by each
primitives and registered to the framework. Note that this only
implements the framework part. Following commit will implement the
action applications for virtual address spaces primitives.
Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rienjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:22 +00:00
|
|
|
continue;
|
2021-11-05 20:47:16 +00:00
|
|
|
|
2022-10-26 22:59:33 +00:00
|
|
|
damos_apply_scheme(c, t, r, s);
|
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS)
In many cases, users might use DAMON for simple data access aware memory
management optimizations such as applying an operation scheme to a
memory region of a specific size having a specific access frequency for
a specific time. For example, "page out a memory region larger than 100
MiB but having a low access frequency more than 10 minutes", or "Use THP
for a memory region larger than 2 MiB having a high access frequency for
more than 2 seconds".
Most simple form of the solution would be doing offline data access
pattern profiling using DAMON and modifying the application source code
or system configuration based on the profiling results. Or, developing
a daemon constructed with two modules (one for access monitoring and the
other for applying memory management actions via mlock(), madvise(),
sysctl, etc) is imaginable.
To avoid users spending their time for implementation of such simple
data access monitoring-based operation schemes, this makes DAMON to
handle such schemes directly. With this change, users can simply
specify their desired schemes to DAMON. Then, DAMON will automatically
apply the schemes to the user-specified target processes.
Each of the schemes is composed with conditions for filtering of the
target memory regions and desired memory management action for the
target. Specifically, the format is::
<min/max size> <min/max access frequency> <min/max age> <action>
The filtering conditions are size of memory region, number of accesses
to the region monitored by DAMON, and the age of the region. The age of
region is incremented periodically but reset when its addresses or
access frequency has significantly changed or the action of a scheme was
applied. For the action, current implementation supports a few of
madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``,
and ``NOHUGEPAGE``.
Because DAMON supports various address spaces and application of the
actions to a monitoring target region is dependent to the type of the
target address space, the application code should be implemented by each
primitives and registered to the framework. Note that this only
implements the framework part. Following commit will implement the
action applications for virtual address spaces primitives.
Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rienjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:22 +00:00
|
|
|
}
|
|
|
|
}
|
|
|
|
|
2021-11-05 20:47:23 +00:00
|
|
|
/* Shouldn't be called if quota->ms and quota->sz are zero */
|
|
|
|
static void damos_set_effective_quota(struct damos_quota *quota)
|
|
|
|
{
|
|
|
|
unsigned long throughput;
|
|
|
|
unsigned long esz;
|
|
|
|
|
|
|
|
if (!quota->ms) {
|
|
|
|
quota->esz = quota->sz;
|
|
|
|
return;
|
|
|
|
}
|
|
|
|
|
|
|
|
if (quota->total_charged_ns)
|
|
|
|
throughput = quota->total_charged_sz * 1000000 /
|
|
|
|
quota->total_charged_ns;
|
|
|
|
else
|
|
|
|
throughput = PAGE_SIZE * 1024;
|
|
|
|
esz = throughput * quota->ms;
|
|
|
|
|
|
|
|
if (quota->sz && quota->sz < esz)
|
|
|
|
esz = quota->sz;
|
|
|
|
quota->esz = esz;
|
|
|
|
}
|
|
|
|
|
2022-10-26 22:59:35 +00:00
|
|
|
static void damos_adjust_quota(struct damon_ctx *c, struct damos *s)
|
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS)
In many cases, users might use DAMON for simple data access aware memory
management optimizations such as applying an operation scheme to a
memory region of a specific size having a specific access frequency for
a specific time. For example, "page out a memory region larger than 100
MiB but having a low access frequency more than 10 minutes", or "Use THP
for a memory region larger than 2 MiB having a high access frequency for
more than 2 seconds".
Most simple form of the solution would be doing offline data access
pattern profiling using DAMON and modifying the application source code
or system configuration based on the profiling results. Or, developing
a daemon constructed with two modules (one for access monitoring and the
other for applying memory management actions via mlock(), madvise(),
sysctl, etc) is imaginable.
To avoid users spending their time for implementation of such simple
data access monitoring-based operation schemes, this makes DAMON to
handle such schemes directly. With this change, users can simply
specify their desired schemes to DAMON. Then, DAMON will automatically
apply the schemes to the user-specified target processes.
Each of the schemes is composed with conditions for filtering of the
target memory regions and desired memory management action for the
target. Specifically, the format is::
<min/max size> <min/max access frequency> <min/max age> <action>
The filtering conditions are size of memory region, number of accesses
to the region monitored by DAMON, and the age of the region. The age of
region is incremented periodically but reset when its addresses or
access frequency has significantly changed or the action of a scheme was
applied. For the action, current implementation supports a few of
madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``,
and ``NOHUGEPAGE``.
Because DAMON supports various address spaces and application of the
actions to a monitoring target region is dependent to the type of the
target address space, the application code should be implemented by each
primitives and registered to the framework. Note that this only
implements the framework part. Following commit will implement the
action applications for virtual address spaces primitives.
Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rienjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:22 +00:00
|
|
|
{
|
2022-10-26 22:59:35 +00:00
|
|
|
struct damos_quota *quota = &s->quota;
|
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS)
In many cases, users might use DAMON for simple data access aware memory
management optimizations such as applying an operation scheme to a
memory region of a specific size having a specific access frequency for
a specific time. For example, "page out a memory region larger than 100
MiB but having a low access frequency more than 10 minutes", or "Use THP
for a memory region larger than 2 MiB having a high access frequency for
more than 2 seconds".
Most simple form of the solution would be doing offline data access
pattern profiling using DAMON and modifying the application source code
or system configuration based on the profiling results. Or, developing
a daemon constructed with two modules (one for access monitoring and the
other for applying memory management actions via mlock(), madvise(),
sysctl, etc) is imaginable.
To avoid users spending their time for implementation of such simple
data access monitoring-based operation schemes, this makes DAMON to
handle such schemes directly. With this change, users can simply
specify their desired schemes to DAMON. Then, DAMON will automatically
apply the schemes to the user-specified target processes.
Each of the schemes is composed with conditions for filtering of the
target memory regions and desired memory management action for the
target. Specifically, the format is::
<min/max size> <min/max access frequency> <min/max age> <action>
The filtering conditions are size of memory region, number of accesses
to the region monitored by DAMON, and the age of the region. The age of
region is incremented periodically but reset when its addresses or
access frequency has significantly changed or the action of a scheme was
applied. For the action, current implementation supports a few of
madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``,
and ``NOHUGEPAGE``.
Because DAMON supports various address spaces and application of the
actions to a monitoring target region is dependent to the type of the
target address space, the application code should be implemented by each
primitives and registered to the framework. Note that this only
implements the framework part. Following commit will implement the
action applications for virtual address spaces primitives.
Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rienjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:22 +00:00
|
|
|
struct damon_target *t;
|
2022-10-26 22:59:35 +00:00
|
|
|
struct damon_region *r;
|
|
|
|
unsigned long cumulated_sz;
|
|
|
|
unsigned int score, max_score = 0;
|
2021-11-05 20:47:16 +00:00
|
|
|
|
2022-10-26 22:59:35 +00:00
|
|
|
if (!quota->ms && !quota->sz)
|
|
|
|
return;
|
2021-11-05 20:47:16 +00:00
|
|
|
|
2022-10-26 22:59:35 +00:00
|
|
|
/* New charge window starts */
|
|
|
|
if (time_after_eq(jiffies, quota->charged_from +
|
|
|
|
msecs_to_jiffies(quota->reset_interval))) {
|
|
|
|
if (quota->esz && quota->charged_sz >= quota->esz)
|
|
|
|
s->stat.qt_exceeds++;
|
|
|
|
quota->total_charged_sz += quota->charged_sz;
|
|
|
|
quota->charged_from = jiffies;
|
|
|
|
quota->charged_sz = 0;
|
|
|
|
damos_set_effective_quota(quota);
|
|
|
|
}
|
mm/damon/schemes: activate schemes based on a watermarks mechanism
DAMON-based operation schemes need to be manually turned on and off. In
some use cases, however, the condition for turning a scheme on and off
would depend on the system's situation. For example, schemes for
proactive pages reclamation would need to be turned on when some memory
pressure is detected, and turned off when the system has enough free
memory.
For easier control of schemes activation based on the system situation,
this introduces a watermarks-based mechanism. The client can describe
the watermark metric (e.g., amount of free memory in the system),
watermark check interval, and three watermarks, namely high, mid, and
low. If the scheme is deactivated, it only gets the metric and compare
that to the three watermarks for every check interval. If the metric is
higher than the high watermark, the scheme is deactivated. If the
metric is between the mid watermark and the low watermark, the scheme is
activated. If the metric is lower than the low watermark, the scheme is
deactivated again. This is to allow users fall back to traditional
page-granularity mechanisms.
Link: https://lkml.kernel.org/r/20211019150731.16699-12-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:47:47 +00:00
|
|
|
|
2022-10-26 22:59:35 +00:00
|
|
|
if (!c->ops.get_scheme_score)
|
|
|
|
return;
|
2021-11-05 20:47:16 +00:00
|
|
|
|
2022-10-26 22:59:35 +00:00
|
|
|
/* Fill up the score histogram */
|
|
|
|
memset(quota->histogram, 0, sizeof(quota->histogram));
|
|
|
|
damon_for_each_target(t, c) {
|
|
|
|
damon_for_each_region(r, t) {
|
|
|
|
if (!__damos_valid_target(r, s))
|
|
|
|
continue;
|
|
|
|
score = c->ops.get_scheme_score(c, t, r, s);
|
|
|
|
quota->histogram[score] += damon_sz_region(r);
|
|
|
|
if (score > max_score)
|
|
|
|
max_score = score;
|
2021-11-05 20:47:16 +00:00
|
|
|
}
|
2022-10-26 22:59:35 +00:00
|
|
|
}
|
2021-11-05 20:47:33 +00:00
|
|
|
|
2022-10-26 22:59:35 +00:00
|
|
|
/* Set the min score limit */
|
|
|
|
for (cumulated_sz = 0, score = max_score; ; score--) {
|
|
|
|
cumulated_sz += quota->histogram[score];
|
|
|
|
if (cumulated_sz >= quota->esz || !score)
|
|
|
|
break;
|
|
|
|
}
|
|
|
|
quota->min_score = score;
|
|
|
|
}
|
2021-11-05 20:47:33 +00:00
|
|
|
|
2022-10-26 22:59:35 +00:00
|
|
|
static void kdamond_apply_schemes(struct damon_ctx *c)
|
|
|
|
{
|
|
|
|
struct damon_target *t;
|
|
|
|
struct damon_region *r, *next_r;
|
|
|
|
struct damos *s;
|
mm/damon/core: implement scheme-specific apply interval
DAMON-based operation schemes are applied for every aggregation interval.
That was mainly because schemes were using nr_accesses, which be complete
to be used for every aggregation interval. However, the schemes are now
using nr_accesses_bp, which is updated for each sampling interval in a way
that reasonable to be used. Therefore, there is no reason to apply
schemes for each aggregation interval.
The unnecessary alignment with aggregation interval was also making some
use cases of DAMOS tricky. Quotas setting under long aggregation interval
is one such example. Suppose the aggregation interval is ten seconds, and
there is a scheme having CPU quota 100ms per 1s. The scheme will actually
uses 100ms per ten seconds, since it cannobe be applied before next
aggregation interval. The feature is working as intended, but the results
might not that intuitive for some users. This could be fixed by updating
the quota to 1s per 10s. But, in the case, the CPU usage of DAMOS could
look like spikes, and would actually make a bad effect to other
CPU-sensitive workloads.
Implement a dedicated timing interval for each DAMON-based operation
scheme, namely apply_interval. The interval will be sampling interval
aligned, and each scheme will be applied for its apply_interval. The
interval is set to 0 by default, and it means the scheme should use the
aggregation interval instead. This avoids old users getting any
behavioral difference.
Link: https://lkml.kernel.org/r/20230916020945.47296-5-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (Google) <rostedt@goodmis.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-16 02:09:40 +00:00
|
|
|
unsigned long sample_interval = c->attrs.sample_interval ?
|
|
|
|
c->attrs.sample_interval : 1;
|
|
|
|
bool has_schemes_to_apply = false;
|
2021-11-05 20:47:33 +00:00
|
|
|
|
2022-10-26 22:59:35 +00:00
|
|
|
damon_for_each_scheme(s, c) {
|
mm/damon/core: implement scheme-specific apply interval
DAMON-based operation schemes are applied for every aggregation interval.
That was mainly because schemes were using nr_accesses, which be complete
to be used for every aggregation interval. However, the schemes are now
using nr_accesses_bp, which is updated for each sampling interval in a way
that reasonable to be used. Therefore, there is no reason to apply
schemes for each aggregation interval.
The unnecessary alignment with aggregation interval was also making some
use cases of DAMOS tricky. Quotas setting under long aggregation interval
is one such example. Suppose the aggregation interval is ten seconds, and
there is a scheme having CPU quota 100ms per 1s. The scheme will actually
uses 100ms per ten seconds, since it cannobe be applied before next
aggregation interval. The feature is working as intended, but the results
might not that intuitive for some users. This could be fixed by updating
the quota to 1s per 10s. But, in the case, the CPU usage of DAMOS could
look like spikes, and would actually make a bad effect to other
CPU-sensitive workloads.
Implement a dedicated timing interval for each DAMON-based operation
scheme, namely apply_interval. The interval will be sampling interval
aligned, and each scheme will be applied for its apply_interval. The
interval is set to 0 by default, and it means the scheme should use the
aggregation interval instead. This avoids old users getting any
behavioral difference.
Link: https://lkml.kernel.org/r/20230916020945.47296-5-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (Google) <rostedt@goodmis.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-16 02:09:40 +00:00
|
|
|
if (c->passed_sample_intervals != s->next_apply_sis)
|
|
|
|
continue;
|
|
|
|
|
|
|
|
s->next_apply_sis +=
|
|
|
|
(s->apply_interval_us ? s->apply_interval_us :
|
|
|
|
c->attrs.aggr_interval) / sample_interval;
|
|
|
|
|
2022-10-26 22:59:35 +00:00
|
|
|
if (!s->wmarks.activated)
|
|
|
|
continue;
|
|
|
|
|
mm/damon/core: implement scheme-specific apply interval
DAMON-based operation schemes are applied for every aggregation interval.
That was mainly because schemes were using nr_accesses, which be complete
to be used for every aggregation interval. However, the schemes are now
using nr_accesses_bp, which is updated for each sampling interval in a way
that reasonable to be used. Therefore, there is no reason to apply
schemes for each aggregation interval.
The unnecessary alignment with aggregation interval was also making some
use cases of DAMOS tricky. Quotas setting under long aggregation interval
is one such example. Suppose the aggregation interval is ten seconds, and
there is a scheme having CPU quota 100ms per 1s. The scheme will actually
uses 100ms per ten seconds, since it cannobe be applied before next
aggregation interval. The feature is working as intended, but the results
might not that intuitive for some users. This could be fixed by updating
the quota to 1s per 10s. But, in the case, the CPU usage of DAMOS could
look like spikes, and would actually make a bad effect to other
CPU-sensitive workloads.
Implement a dedicated timing interval for each DAMON-based operation
scheme, namely apply_interval. The interval will be sampling interval
aligned, and each scheme will be applied for its apply_interval. The
interval is set to 0 by default, and it means the scheme should use the
aggregation interval instead. This avoids old users getting any
behavioral difference.
Link: https://lkml.kernel.org/r/20230916020945.47296-5-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (Google) <rostedt@goodmis.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-16 02:09:40 +00:00
|
|
|
has_schemes_to_apply = true;
|
|
|
|
|
2022-10-26 22:59:35 +00:00
|
|
|
damos_adjust_quota(c, s);
|
2021-11-05 20:47:16 +00:00
|
|
|
}
|
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS)
In many cases, users might use DAMON for simple data access aware memory
management optimizations such as applying an operation scheme to a
memory region of a specific size having a specific access frequency for
a specific time. For example, "page out a memory region larger than 100
MiB but having a low access frequency more than 10 minutes", or "Use THP
for a memory region larger than 2 MiB having a high access frequency for
more than 2 seconds".
Most simple form of the solution would be doing offline data access
pattern profiling using DAMON and modifying the application source code
or system configuration based on the profiling results. Or, developing
a daemon constructed with two modules (one for access monitoring and the
other for applying memory management actions via mlock(), madvise(),
sysctl, etc) is imaginable.
To avoid users spending their time for implementation of such simple
data access monitoring-based operation schemes, this makes DAMON to
handle such schemes directly. With this change, users can simply
specify their desired schemes to DAMON. Then, DAMON will automatically
apply the schemes to the user-specified target processes.
Each of the schemes is composed with conditions for filtering of the
target memory regions and desired memory management action for the
target. Specifically, the format is::
<min/max size> <min/max access frequency> <min/max age> <action>
The filtering conditions are size of memory region, number of accesses
to the region monitored by DAMON, and the age of the region. The age of
region is incremented periodically but reset when its addresses or
access frequency has significantly changed or the action of a scheme was
applied. For the action, current implementation supports a few of
madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``,
and ``NOHUGEPAGE``.
Because DAMON supports various address spaces and application of the
actions to a monitoring target region is dependent to the type of the
target address space, the application code should be implemented by each
primitives and registered to the framework. Note that this only
implements the framework part. Following commit will implement the
action applications for virtual address spaces primitives.
Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rienjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:22 +00:00
|
|
|
|
mm/damon/core: implement scheme-specific apply interval
DAMON-based operation schemes are applied for every aggregation interval.
That was mainly because schemes were using nr_accesses, which be complete
to be used for every aggregation interval. However, the schemes are now
using nr_accesses_bp, which is updated for each sampling interval in a way
that reasonable to be used. Therefore, there is no reason to apply
schemes for each aggregation interval.
The unnecessary alignment with aggregation interval was also making some
use cases of DAMOS tricky. Quotas setting under long aggregation interval
is one such example. Suppose the aggregation interval is ten seconds, and
there is a scheme having CPU quota 100ms per 1s. The scheme will actually
uses 100ms per ten seconds, since it cannobe be applied before next
aggregation interval. The feature is working as intended, but the results
might not that intuitive for some users. This could be fixed by updating
the quota to 1s per 10s. But, in the case, the CPU usage of DAMOS could
look like spikes, and would actually make a bad effect to other
CPU-sensitive workloads.
Implement a dedicated timing interval for each DAMON-based operation
scheme, namely apply_interval. The interval will be sampling interval
aligned, and each scheme will be applied for its apply_interval. The
interval is set to 0 by default, and it means the scheme should use the
aggregation interval instead. This avoids old users getting any
behavioral difference.
Link: https://lkml.kernel.org/r/20230916020945.47296-5-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (Google) <rostedt@goodmis.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-16 02:09:40 +00:00
|
|
|
if (!has_schemes_to_apply)
|
|
|
|
return;
|
|
|
|
|
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS)
In many cases, users might use DAMON for simple data access aware memory
management optimizations such as applying an operation scheme to a
memory region of a specific size having a specific access frequency for
a specific time. For example, "page out a memory region larger than 100
MiB but having a low access frequency more than 10 minutes", or "Use THP
for a memory region larger than 2 MiB having a high access frequency for
more than 2 seconds".
Most simple form of the solution would be doing offline data access
pattern profiling using DAMON and modifying the application source code
or system configuration based on the profiling results. Or, developing
a daemon constructed with two modules (one for access monitoring and the
other for applying memory management actions via mlock(), madvise(),
sysctl, etc) is imaginable.
To avoid users spending their time for implementation of such simple
data access monitoring-based operation schemes, this makes DAMON to
handle such schemes directly. With this change, users can simply
specify their desired schemes to DAMON. Then, DAMON will automatically
apply the schemes to the user-specified target processes.
Each of the schemes is composed with conditions for filtering of the
target memory regions and desired memory management action for the
target. Specifically, the format is::
<min/max size> <min/max access frequency> <min/max age> <action>
The filtering conditions are size of memory region, number of accesses
to the region monitored by DAMON, and the age of the region. The age of
region is incremented periodically but reset when its addresses or
access frequency has significantly changed or the action of a scheme was
applied. For the action, current implementation supports a few of
madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``,
and ``NOHUGEPAGE``.
Because DAMON supports various address spaces and application of the
actions to a monitoring target region is dependent to the type of the
target address space, the application code should be implemented by each
primitives and registered to the framework. Note that this only
implements the framework part. Following commit will implement the
action applications for virtual address spaces primitives.
Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rienjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:22 +00:00
|
|
|
damon_for_each_target(t, c) {
|
2021-11-05 20:47:16 +00:00
|
|
|
damon_for_each_region_safe(r, next_r, t)
|
mm/damon/core: implement DAMON-based Operation Schemes (DAMOS)
In many cases, users might use DAMON for simple data access aware memory
management optimizations such as applying an operation scheme to a
memory region of a specific size having a specific access frequency for
a specific time. For example, "page out a memory region larger than 100
MiB but having a low access frequency more than 10 minutes", or "Use THP
for a memory region larger than 2 MiB having a high access frequency for
more than 2 seconds".
Most simple form of the solution would be doing offline data access
pattern profiling using DAMON and modifying the application source code
or system configuration based on the profiling results. Or, developing
a daemon constructed with two modules (one for access monitoring and the
other for applying memory management actions via mlock(), madvise(),
sysctl, etc) is imaginable.
To avoid users spending their time for implementation of such simple
data access monitoring-based operation schemes, this makes DAMON to
handle such schemes directly. With this change, users can simply
specify their desired schemes to DAMON. Then, DAMON will automatically
apply the schemes to the user-specified target processes.
Each of the schemes is composed with conditions for filtering of the
target memory regions and desired memory management action for the
target. Specifically, the format is::
<min/max size> <min/max access frequency> <min/max age> <action>
The filtering conditions are size of memory region, number of accesses
to the region monitored by DAMON, and the age of the region. The age of
region is incremented periodically but reset when its addresses or
access frequency has significantly changed or the action of a scheme was
applied. For the action, current implementation supports a few of
madvise()-like hints, ``WILLNEED``, ``COLD``, ``PAGEOUT``, ``HUGEPAGE``,
and ``NOHUGEPAGE``.
Because DAMON supports various address spaces and application of the
actions to a monitoring target region is dependent to the type of the
target address space, the application code should be implemented by each
primitives and registered to the framework. Note that this only
implements the framework part. Following commit will implement the
action applications for virtual address spaces primitives.
Link: https://lkml.kernel.org/r/20211001125604.29660-3-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rienjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:22 +00:00
|
|
|
damon_do_apply_schemes(c, t, r);
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
2021-09-08 02:56:36 +00:00
|
|
|
/*
|
|
|
|
* Merge two adjacent regions into one region
|
|
|
|
*/
|
|
|
|
static void damon_merge_two_regions(struct damon_target *t,
|
|
|
|
struct damon_region *l, struct damon_region *r)
|
|
|
|
{
|
2022-09-27 00:19:45 +00:00
|
|
|
unsigned long sz_l = damon_sz_region(l), sz_r = damon_sz_region(r);
|
2021-09-08 02:56:36 +00:00
|
|
|
|
|
|
|
l->nr_accesses = (l->nr_accesses * sz_l + r->nr_accesses * sz_r) /
|
|
|
|
(sz_l + sz_r);
|
2023-09-15 02:52:48 +00:00
|
|
|
l->nr_accesses_bp = l->nr_accesses * 10000;
|
mm/damon/core: account age of target regions
Patch series "Implement Data Access Monitoring-based Memory Operation Schemes".
Introduction
============
DAMON[1] can be used as a primitive for data access aware memory
management optimizations. For that, users who want such optimizations
should run DAMON, read the monitoring results, analyze it, plan a new
memory management scheme, and apply the new scheme by themselves. Such
efforts will be inevitable for some complicated optimizations.
However, in many other cases, the users would simply want the system to
apply a memory management action to a memory region of a specific size
having a specific access frequency for a specific time. For example,
"page out a memory region larger than 100 MiB keeping only rare accesses
more than 2 minutes", or "Do not use THP for a memory region larger than
2 MiB rarely accessed for more than 1 seconds".
To make the works easier and non-redundant, this patchset implements a
new feature of DAMON, which is called Data Access Monitoring-based
Operation Schemes (DAMOS). Using the feature, users can describe the
normal schemes in a simple way and ask DAMON to execute those on its
own.
[1] https://damonitor.github.io
Evaluations
===========
DAMOS is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation
are not for production but only for proof of concepts.
Please refer to the showcase web site's evaluation document[1] for
detailed evaluation setup and results.
[1] https://damonitor.github.io/doc/html/v34/vm/damon/eval.html
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are
another couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://git.kernel.org/sj/h/damon/for-v5.4.y
- For v5.10.y: https://git.kernel.org/sj/h/damon/for-v5.10.y
Sequence Of Patches
===================
The 1st patch accounts age of each region. The 2nd patch implements the
core of the DAMON-based operation schemes feature. The 3rd patch makes
the default monitoring primitives for virtual address spaces to support
the schemes. From this point, the kernel space users can use DAMOS.
The 4th patch exports the feature to the user space via the debugfs
interface. The 5th patch implements schemes statistics feature for
easier tuning of the schemes and runtime access pattern analysis, and
the 6th patch adds selftests for these changes. Finally, the 7th patch
documents this new feature.
This patch (of 7):
DAMON can be used for data access pattern aware memory management
optimizations. For that, users should run DAMON, read the monitoring
results, analyze it, plan a new memory management scheme, and apply the
new scheme by themselves. It would not be too hard, but still require
some level of effort. For complicated cases, this effort is inevitable.
That said, in many cases, users would simply want to apply an actions to
a memory region of a specific size having a specific access frequency
for a specific time. For example, "page out a memory region larger than
100 MiB but having a low access frequency more than 10 minutes", or "Use
THP for a memory region larger than 2 MiB having a high access frequency
for more than 2 seconds".
For such optimizations, users will need to first account the age of each
region themselves. To reduce such efforts, this implements a simple age
account of each region in DAMON. For each aggregation step, DAMON
compares the access frequency with that from last aggregation and reset
the age of the region if the change is significant. Else, the age is
incremented. Also, in case of the merge of regions, the region
size-weighted average of the ages is set as the age of merged new
region.
Link: https://lkml.kernel.org/r/20211001125604.29660-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20211001125604.29660-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Greg Thelen <gthelen@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: David Rienjes <rientjes@google.com>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:18 +00:00
|
|
|
l->age = (l->age * sz_l + r->age * sz_r) / (sz_l + sz_r);
|
2021-09-08 02:56:36 +00:00
|
|
|
l->ar.end = r->ar.end;
|
|
|
|
damon_destroy_region(r, t);
|
|
|
|
}
|
|
|
|
|
|
|
|
/*
|
|
|
|
* Merge adjacent regions having similar access frequencies
|
|
|
|
*
|
|
|
|
* t target affected by this merge operation
|
|
|
|
* thres '->nr_accesses' diff threshold for the merge
|
|
|
|
* sz_limit size upper limit of each region
|
|
|
|
*/
|
|
|
|
static void damon_merge_regions_of(struct damon_target *t, unsigned int thres,
|
|
|
|
unsigned long sz_limit)
|
|
|
|
{
|
|
|
|
struct damon_region *r, *prev = NULL, *next;
|
|
|
|
|
|
|
|
damon_for_each_region_safe(r, next, t) {
|
2022-01-14 22:09:40 +00:00
|
|
|
if (abs(r->nr_accesses - r->last_nr_accesses) > thres)
|
mm/damon/core: account age of target regions
Patch series "Implement Data Access Monitoring-based Memory Operation Schemes".
Introduction
============
DAMON[1] can be used as a primitive for data access aware memory
management optimizations. For that, users who want such optimizations
should run DAMON, read the monitoring results, analyze it, plan a new
memory management scheme, and apply the new scheme by themselves. Such
efforts will be inevitable for some complicated optimizations.
However, in many other cases, the users would simply want the system to
apply a memory management action to a memory region of a specific size
having a specific access frequency for a specific time. For example,
"page out a memory region larger than 100 MiB keeping only rare accesses
more than 2 minutes", or "Do not use THP for a memory region larger than
2 MiB rarely accessed for more than 1 seconds".
To make the works easier and non-redundant, this patchset implements a
new feature of DAMON, which is called Data Access Monitoring-based
Operation Schemes (DAMOS). Using the feature, users can describe the
normal schemes in a simple way and ask DAMON to execute those on its
own.
[1] https://damonitor.github.io
Evaluations
===========
DAMOS is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation
are not for production but only for proof of concepts.
Please refer to the showcase web site's evaluation document[1] for
detailed evaluation setup and results.
[1] https://damonitor.github.io/doc/html/v34/vm/damon/eval.html
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are
another couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://git.kernel.org/sj/h/damon/for-v5.4.y
- For v5.10.y: https://git.kernel.org/sj/h/damon/for-v5.10.y
Sequence Of Patches
===================
The 1st patch accounts age of each region. The 2nd patch implements the
core of the DAMON-based operation schemes feature. The 3rd patch makes
the default monitoring primitives for virtual address spaces to support
the schemes. From this point, the kernel space users can use DAMOS.
The 4th patch exports the feature to the user space via the debugfs
interface. The 5th patch implements schemes statistics feature for
easier tuning of the schemes and runtime access pattern analysis, and
the 6th patch adds selftests for these changes. Finally, the 7th patch
documents this new feature.
This patch (of 7):
DAMON can be used for data access pattern aware memory management
optimizations. For that, users should run DAMON, read the monitoring
results, analyze it, plan a new memory management scheme, and apply the
new scheme by themselves. It would not be too hard, but still require
some level of effort. For complicated cases, this effort is inevitable.
That said, in many cases, users would simply want to apply an actions to
a memory region of a specific size having a specific access frequency
for a specific time. For example, "page out a memory region larger than
100 MiB but having a low access frequency more than 10 minutes", or "Use
THP for a memory region larger than 2 MiB having a high access frequency
for more than 2 seconds".
For such optimizations, users will need to first account the age of each
region themselves. To reduce such efforts, this implements a simple age
account of each region in DAMON. For each aggregation step, DAMON
compares the access frequency with that from last aggregation and reset
the age of the region if the change is significant. Else, the age is
incremented. Also, in case of the merge of regions, the region
size-weighted average of the ages is set as the age of merged new
region.
Link: https://lkml.kernel.org/r/20211001125604.29660-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20211001125604.29660-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Greg Thelen <gthelen@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: David Rienjes <rientjes@google.com>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:18 +00:00
|
|
|
r->age = 0;
|
|
|
|
else
|
|
|
|
r->age++;
|
|
|
|
|
2021-09-08 02:56:36 +00:00
|
|
|
if (prev && prev->ar.end == r->ar.start &&
|
2022-01-14 22:09:40 +00:00
|
|
|
abs(prev->nr_accesses - r->nr_accesses) <= thres &&
|
2022-09-27 00:19:45 +00:00
|
|
|
damon_sz_region(prev) + damon_sz_region(r) <= sz_limit)
|
2021-09-08 02:56:36 +00:00
|
|
|
damon_merge_two_regions(t, prev, r);
|
|
|
|
else
|
|
|
|
prev = r;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
/*
|
|
|
|
* Merge adjacent regions having similar access frequencies
|
|
|
|
*
|
|
|
|
* threshold '->nr_accesses' diff threshold for the merge
|
|
|
|
* sz_limit size upper limit of each region
|
|
|
|
*
|
|
|
|
* This function merges monitoring target regions which are adjacent and their
|
|
|
|
* access frequencies are similar. This is for minimizing the monitoring
|
|
|
|
* overhead under the dynamically changeable access pattern. If a merge was
|
|
|
|
* unnecessarily made, later 'kdamond_split_regions()' will revert it.
|
|
|
|
*/
|
|
|
|
static void kdamond_merge_regions(struct damon_ctx *c, unsigned int threshold,
|
|
|
|
unsigned long sz_limit)
|
|
|
|
{
|
|
|
|
struct damon_target *t;
|
|
|
|
|
|
|
|
damon_for_each_target(t, c)
|
|
|
|
damon_merge_regions_of(t, threshold, sz_limit);
|
|
|
|
}
|
|
|
|
|
|
|
|
/*
|
|
|
|
* Split a region in two
|
|
|
|
*
|
|
|
|
* r the region to be split
|
|
|
|
* sz_r size of the first sub-region that will be made
|
|
|
|
*/
|
2022-08-13 15:19:03 +00:00
|
|
|
static void damon_split_region_at(struct damon_target *t,
|
|
|
|
struct damon_region *r, unsigned long sz_r)
|
2021-09-08 02:56:36 +00:00
|
|
|
{
|
|
|
|
struct damon_region *new;
|
|
|
|
|
|
|
|
new = damon_new_region(r->ar.start + sz_r, r->ar.end);
|
|
|
|
if (!new)
|
|
|
|
return;
|
|
|
|
|
|
|
|
r->ar.end = new->ar.start;
|
|
|
|
|
mm/damon/core: account age of target regions
Patch series "Implement Data Access Monitoring-based Memory Operation Schemes".
Introduction
============
DAMON[1] can be used as a primitive for data access aware memory
management optimizations. For that, users who want such optimizations
should run DAMON, read the monitoring results, analyze it, plan a new
memory management scheme, and apply the new scheme by themselves. Such
efforts will be inevitable for some complicated optimizations.
However, in many other cases, the users would simply want the system to
apply a memory management action to a memory region of a specific size
having a specific access frequency for a specific time. For example,
"page out a memory region larger than 100 MiB keeping only rare accesses
more than 2 minutes", or "Do not use THP for a memory region larger than
2 MiB rarely accessed for more than 1 seconds".
To make the works easier and non-redundant, this patchset implements a
new feature of DAMON, which is called Data Access Monitoring-based
Operation Schemes (DAMOS). Using the feature, users can describe the
normal schemes in a simple way and ask DAMON to execute those on its
own.
[1] https://damonitor.github.io
Evaluations
===========
DAMOS is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation
are not for production but only for proof of concepts.
Please refer to the showcase web site's evaluation document[1] for
detailed evaluation setup and results.
[1] https://damonitor.github.io/doc/html/v34/vm/damon/eval.html
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are
another couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://git.kernel.org/sj/h/damon/for-v5.4.y
- For v5.10.y: https://git.kernel.org/sj/h/damon/for-v5.10.y
Sequence Of Patches
===================
The 1st patch accounts age of each region. The 2nd patch implements the
core of the DAMON-based operation schemes feature. The 3rd patch makes
the default monitoring primitives for virtual address spaces to support
the schemes. From this point, the kernel space users can use DAMOS.
The 4th patch exports the feature to the user space via the debugfs
interface. The 5th patch implements schemes statistics feature for
easier tuning of the schemes and runtime access pattern analysis, and
the 6th patch adds selftests for these changes. Finally, the 7th patch
documents this new feature.
This patch (of 7):
DAMON can be used for data access pattern aware memory management
optimizations. For that, users should run DAMON, read the monitoring
results, analyze it, plan a new memory management scheme, and apply the
new scheme by themselves. It would not be too hard, but still require
some level of effort. For complicated cases, this effort is inevitable.
That said, in many cases, users would simply want to apply an actions to
a memory region of a specific size having a specific access frequency
for a specific time. For example, "page out a memory region larger than
100 MiB but having a low access frequency more than 10 minutes", or "Use
THP for a memory region larger than 2 MiB having a high access frequency
for more than 2 seconds".
For such optimizations, users will need to first account the age of each
region themselves. To reduce such efforts, this implements a simple age
account of each region in DAMON. For each aggregation step, DAMON
compares the access frequency with that from last aggregation and reset
the age of the region if the change is significant. Else, the age is
incremented. Also, in case of the merge of regions, the region
size-weighted average of the ages is set as the age of merged new
region.
Link: https://lkml.kernel.org/r/20211001125604.29660-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20211001125604.29660-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Greg Thelen <gthelen@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: David Rienjes <rientjes@google.com>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:18 +00:00
|
|
|
new->age = r->age;
|
|
|
|
new->last_nr_accesses = r->last_nr_accesses;
|
2023-09-15 02:52:48 +00:00
|
|
|
new->nr_accesses_bp = r->nr_accesses_bp;
|
mm/damon/core: account age of target regions
Patch series "Implement Data Access Monitoring-based Memory Operation Schemes".
Introduction
============
DAMON[1] can be used as a primitive for data access aware memory
management optimizations. For that, users who want such optimizations
should run DAMON, read the monitoring results, analyze it, plan a new
memory management scheme, and apply the new scheme by themselves. Such
efforts will be inevitable for some complicated optimizations.
However, in many other cases, the users would simply want the system to
apply a memory management action to a memory region of a specific size
having a specific access frequency for a specific time. For example,
"page out a memory region larger than 100 MiB keeping only rare accesses
more than 2 minutes", or "Do not use THP for a memory region larger than
2 MiB rarely accessed for more than 1 seconds".
To make the works easier and non-redundant, this patchset implements a
new feature of DAMON, which is called Data Access Monitoring-based
Operation Schemes (DAMOS). Using the feature, users can describe the
normal schemes in a simple way and ask DAMON to execute those on its
own.
[1] https://damonitor.github.io
Evaluations
===========
DAMOS is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation
are not for production but only for proof of concepts.
Please refer to the showcase web site's evaluation document[1] for
detailed evaluation setup and results.
[1] https://damonitor.github.io/doc/html/v34/vm/damon/eval.html
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are
another couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://git.kernel.org/sj/h/damon/for-v5.4.y
- For v5.10.y: https://git.kernel.org/sj/h/damon/for-v5.10.y
Sequence Of Patches
===================
The 1st patch accounts age of each region. The 2nd patch implements the
core of the DAMON-based operation schemes feature. The 3rd patch makes
the default monitoring primitives for virtual address spaces to support
the schemes. From this point, the kernel space users can use DAMOS.
The 4th patch exports the feature to the user space via the debugfs
interface. The 5th patch implements schemes statistics feature for
easier tuning of the schemes and runtime access pattern analysis, and
the 6th patch adds selftests for these changes. Finally, the 7th patch
documents this new feature.
This patch (of 7):
DAMON can be used for data access pattern aware memory management
optimizations. For that, users should run DAMON, read the monitoring
results, analyze it, plan a new memory management scheme, and apply the
new scheme by themselves. It would not be too hard, but still require
some level of effort. For complicated cases, this effort is inevitable.
That said, in many cases, users would simply want to apply an actions to
a memory region of a specific size having a specific access frequency
for a specific time. For example, "page out a memory region larger than
100 MiB but having a low access frequency more than 10 minutes", or "Use
THP for a memory region larger than 2 MiB having a high access frequency
for more than 2 seconds".
For such optimizations, users will need to first account the age of each
region themselves. To reduce such efforts, this implements a simple age
account of each region in DAMON. For each aggregation step, DAMON
compares the access frequency with that from last aggregation and reset
the age of the region if the change is significant. Else, the age is
incremented. Also, in case of the merge of regions, the region
size-weighted average of the ages is set as the age of merged new
region.
Link: https://lkml.kernel.org/r/20211001125604.29660-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20211001125604.29660-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Greg Thelen <gthelen@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: David Rienjes <rientjes@google.com>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:46:18 +00:00
|
|
|
|
2021-09-08 02:56:36 +00:00
|
|
|
damon_insert_region(new, r, damon_next_region(r), t);
|
|
|
|
}
|
|
|
|
|
|
|
|
/* Split every region in the given target into 'nr_subs' regions */
|
2022-08-13 15:19:03 +00:00
|
|
|
static void damon_split_regions_of(struct damon_target *t, int nr_subs)
|
2021-09-08 02:56:36 +00:00
|
|
|
{
|
|
|
|
struct damon_region *r, *next;
|
|
|
|
unsigned long sz_region, sz_sub = 0;
|
|
|
|
int i;
|
|
|
|
|
|
|
|
damon_for_each_region_safe(r, next, t) {
|
2022-09-27 00:19:46 +00:00
|
|
|
sz_region = damon_sz_region(r);
|
2021-09-08 02:56:36 +00:00
|
|
|
|
|
|
|
for (i = 0; i < nr_subs - 1 &&
|
|
|
|
sz_region > 2 * DAMON_MIN_REGION; i++) {
|
|
|
|
/*
|
|
|
|
* Randomly select size of left sub-region to be at
|
|
|
|
* least 10 percent and at most 90% of original region
|
|
|
|
*/
|
|
|
|
sz_sub = ALIGN_DOWN(damon_rand(1, 10) *
|
|
|
|
sz_region / 10, DAMON_MIN_REGION);
|
|
|
|
/* Do not allow blank region */
|
|
|
|
if (sz_sub == 0 || sz_sub >= sz_region)
|
|
|
|
continue;
|
|
|
|
|
2022-08-13 15:19:03 +00:00
|
|
|
damon_split_region_at(t, r, sz_sub);
|
2021-09-08 02:56:36 +00:00
|
|
|
sz_region = sz_sub;
|
|
|
|
}
|
|
|
|
}
|
|
|
|
}
|
|
|
|
|
|
|
|
/*
|
|
|
|
* Split every target region into randomly-sized small regions
|
|
|
|
*
|
|
|
|
* This function splits every target region into random-sized small regions if
|
|
|
|
* current total number of the regions is equal or smaller than half of the
|
|
|
|
* user-specified maximum number of regions. This is for maximizing the
|
|
|
|
* monitoring accuracy under the dynamically changeable access patterns. If a
|
|
|
|
* split was unnecessarily made, later 'kdamond_merge_regions()' will revert
|
|
|
|
* it.
|
|
|
|
*/
|
|
|
|
static void kdamond_split_regions(struct damon_ctx *ctx)
|
|
|
|
{
|
|
|
|
struct damon_target *t;
|
|
|
|
unsigned int nr_regions = 0;
|
|
|
|
static unsigned int last_nr_regions;
|
|
|
|
int nr_subregions = 2;
|
|
|
|
|
|
|
|
damon_for_each_target(t, ctx)
|
|
|
|
nr_regions += damon_nr_regions(t);
|
|
|
|
|
2022-09-13 17:44:32 +00:00
|
|
|
if (nr_regions > ctx->attrs.max_nr_regions / 2)
|
2021-09-08 02:56:36 +00:00
|
|
|
return;
|
|
|
|
|
|
|
|
/* Maybe the middle of the region has different access frequency */
|
|
|
|
if (last_nr_regions == nr_regions &&
|
2022-09-13 17:44:32 +00:00
|
|
|
nr_regions < ctx->attrs.max_nr_regions / 3)
|
2021-09-08 02:56:36 +00:00
|
|
|
nr_subregions = 3;
|
|
|
|
|
|
|
|
damon_for_each_target(t, ctx)
|
2022-08-13 15:19:03 +00:00
|
|
|
damon_split_regions_of(t, nr_subregions);
|
2021-09-08 02:56:36 +00:00
|
|
|
|
|
|
|
last_nr_regions = nr_regions;
|
|
|
|
}
|
|
|
|
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
/*
|
|
|
|
* Check whether current monitoring should be stopped
|
|
|
|
*
|
|
|
|
* The monitoring is stopped when either the user requested to stop, or all
|
|
|
|
* monitoring targets are invalid.
|
|
|
|
*
|
|
|
|
* Returns true if need to stop current monitoring.
|
|
|
|
*/
|
|
|
|
static bool kdamond_need_stop(struct damon_ctx *ctx)
|
|
|
|
{
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
struct damon_target *t;
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
|
2021-11-05 20:48:22 +00:00
|
|
|
if (kthread_should_stop())
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
return true;
|
|
|
|
|
2022-03-22 21:48:46 +00:00
|
|
|
if (!ctx->ops.target_valid)
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
return false;
|
|
|
|
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
damon_for_each_target(t, ctx) {
|
2022-03-22 21:48:46 +00:00
|
|
|
if (ctx->ops.target_valid(t))
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
return false;
|
|
|
|
}
|
|
|
|
|
|
|
|
return true;
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
}
|
|
|
|
|
mm/damon/schemes: activate schemes based on a watermarks mechanism
DAMON-based operation schemes need to be manually turned on and off. In
some use cases, however, the condition for turning a scheme on and off
would depend on the system's situation. For example, schemes for
proactive pages reclamation would need to be turned on when some memory
pressure is detected, and turned off when the system has enough free
memory.
For easier control of schemes activation based on the system situation,
this introduces a watermarks-based mechanism. The client can describe
the watermark metric (e.g., amount of free memory in the system),
watermark check interval, and three watermarks, namely high, mid, and
low. If the scheme is deactivated, it only gets the metric and compare
that to the three watermarks for every check interval. If the metric is
higher than the high watermark, the scheme is deactivated. If the
metric is between the mid watermark and the low watermark, the scheme is
activated. If the metric is lower than the low watermark, the scheme is
deactivated again. This is to allow users fall back to traditional
page-granularity mechanisms.
Link: https://lkml.kernel.org/r/20211019150731.16699-12-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:47:47 +00:00
|
|
|
static unsigned long damos_wmark_metric_value(enum damos_wmark_metric metric)
|
|
|
|
{
|
|
|
|
switch (metric) {
|
|
|
|
case DAMOS_WMARK_FREE_MEM_RATE:
|
2023-09-20 01:57:27 +00:00
|
|
|
return global_zone_page_state(NR_FREE_PAGES) * 1000 /
|
|
|
|
totalram_pages();
|
mm/damon/schemes: activate schemes based on a watermarks mechanism
DAMON-based operation schemes need to be manually turned on and off. In
some use cases, however, the condition for turning a scheme on and off
would depend on the system's situation. For example, schemes for
proactive pages reclamation would need to be turned on when some memory
pressure is detected, and turned off when the system has enough free
memory.
For easier control of schemes activation based on the system situation,
this introduces a watermarks-based mechanism. The client can describe
the watermark metric (e.g., amount of free memory in the system),
watermark check interval, and three watermarks, namely high, mid, and
low. If the scheme is deactivated, it only gets the metric and compare
that to the three watermarks for every check interval. If the metric is
higher than the high watermark, the scheme is deactivated. If the
metric is between the mid watermark and the low watermark, the scheme is
activated. If the metric is lower than the low watermark, the scheme is
deactivated again. This is to allow users fall back to traditional
page-granularity mechanisms.
Link: https://lkml.kernel.org/r/20211019150731.16699-12-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:47:47 +00:00
|
|
|
default:
|
|
|
|
break;
|
|
|
|
}
|
|
|
|
return -EINVAL;
|
|
|
|
}
|
|
|
|
|
|
|
|
/*
|
|
|
|
* Returns zero if the scheme is active. Else, returns time to wait for next
|
|
|
|
* watermark check in micro-seconds.
|
|
|
|
*/
|
|
|
|
static unsigned long damos_wmark_wait_us(struct damos *scheme)
|
|
|
|
{
|
|
|
|
unsigned long metric;
|
|
|
|
|
|
|
|
if (scheme->wmarks.metric == DAMOS_WMARK_NONE)
|
|
|
|
return 0;
|
|
|
|
|
|
|
|
metric = damos_wmark_metric_value(scheme->wmarks.metric);
|
|
|
|
/* higher than high watermark or lower than low watermark */
|
|
|
|
if (metric > scheme->wmarks.high || scheme->wmarks.low > metric) {
|
|
|
|
if (scheme->wmarks.activated)
|
2021-11-05 20:48:24 +00:00
|
|
|
pr_debug("deactivate a scheme (%d) for %s wmark\n",
|
mm/damon/schemes: activate schemes based on a watermarks mechanism
DAMON-based operation schemes need to be manually turned on and off. In
some use cases, however, the condition for turning a scheme on and off
would depend on the system's situation. For example, schemes for
proactive pages reclamation would need to be turned on when some memory
pressure is detected, and turned off when the system has enough free
memory.
For easier control of schemes activation based on the system situation,
this introduces a watermarks-based mechanism. The client can describe
the watermark metric (e.g., amount of free memory in the system),
watermark check interval, and three watermarks, namely high, mid, and
low. If the scheme is deactivated, it only gets the metric and compare
that to the three watermarks for every check interval. If the metric is
higher than the high watermark, the scheme is deactivated. If the
metric is between the mid watermark and the low watermark, the scheme is
activated. If the metric is lower than the low watermark, the scheme is
deactivated again. This is to allow users fall back to traditional
page-granularity mechanisms.
Link: https://lkml.kernel.org/r/20211019150731.16699-12-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:47:47 +00:00
|
|
|
scheme->action,
|
|
|
|
metric > scheme->wmarks.high ?
|
|
|
|
"high" : "low");
|
|
|
|
scheme->wmarks.activated = false;
|
|
|
|
return scheme->wmarks.interval;
|
|
|
|
}
|
|
|
|
|
|
|
|
/* inactive and higher than middle watermark */
|
|
|
|
if ((scheme->wmarks.high >= metric && metric >= scheme->wmarks.mid) &&
|
|
|
|
!scheme->wmarks.activated)
|
|
|
|
return scheme->wmarks.interval;
|
|
|
|
|
|
|
|
if (!scheme->wmarks.activated)
|
|
|
|
pr_debug("activate a scheme (%d)\n", scheme->action);
|
|
|
|
scheme->wmarks.activated = true;
|
|
|
|
return 0;
|
|
|
|
}
|
|
|
|
|
|
|
|
static void kdamond_usleep(unsigned long usecs)
|
|
|
|
{
|
2021-12-10 22:46:28 +00:00
|
|
|
/* See Documentation/timers/timers-howto.rst for the thresholds */
|
|
|
|
if (usecs > 20 * USEC_PER_MSEC)
|
2021-12-10 22:46:25 +00:00
|
|
|
schedule_timeout_idle(usecs_to_jiffies(usecs));
|
mm/damon/schemes: activate schemes based on a watermarks mechanism
DAMON-based operation schemes need to be manually turned on and off. In
some use cases, however, the condition for turning a scheme on and off
would depend on the system's situation. For example, schemes for
proactive pages reclamation would need to be turned on when some memory
pressure is detected, and turned off when the system has enough free
memory.
For easier control of schemes activation based on the system situation,
this introduces a watermarks-based mechanism. The client can describe
the watermark metric (e.g., amount of free memory in the system),
watermark check interval, and three watermarks, namely high, mid, and
low. If the scheme is deactivated, it only gets the metric and compare
that to the three watermarks for every check interval. If the metric is
higher than the high watermark, the scheme is deactivated. If the
metric is between the mid watermark and the low watermark, the scheme is
activated. If the metric is lower than the low watermark, the scheme is
deactivated again. This is to allow users fall back to traditional
page-granularity mechanisms.
Link: https://lkml.kernel.org/r/20211019150731.16699-12-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:47:47 +00:00
|
|
|
else
|
2021-12-10 22:46:25 +00:00
|
|
|
usleep_idle_range(usecs, usecs + 1);
|
mm/damon/schemes: activate schemes based on a watermarks mechanism
DAMON-based operation schemes need to be manually turned on and off. In
some use cases, however, the condition for turning a scheme on and off
would depend on the system's situation. For example, schemes for
proactive pages reclamation would need to be turned on when some memory
pressure is detected, and turned off when the system has enough free
memory.
For easier control of schemes activation based on the system situation,
this introduces a watermarks-based mechanism. The client can describe
the watermark metric (e.g., amount of free memory in the system),
watermark check interval, and three watermarks, namely high, mid, and
low. If the scheme is deactivated, it only gets the metric and compare
that to the three watermarks for every check interval. If the metric is
higher than the high watermark, the scheme is deactivated. If the
metric is between the mid watermark and the low watermark, the scheme is
activated. If the metric is lower than the low watermark, the scheme is
deactivated again. This is to allow users fall back to traditional
page-granularity mechanisms.
Link: https://lkml.kernel.org/r/20211019150731.16699-12-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:47:47 +00:00
|
|
|
}
|
|
|
|
|
|
|
|
/* Returns negative error code if it's not activated but should return */
|
|
|
|
static int kdamond_wait_activation(struct damon_ctx *ctx)
|
|
|
|
{
|
|
|
|
struct damos *s;
|
|
|
|
unsigned long wait_time;
|
|
|
|
unsigned long min_wait_time = 0;
|
mm/damon: prevent activated scheme from sleeping by deactivated schemes
In the DAMON, the minimum wait time of the schemes decides whether the
kernel wakes up 'kdamon_fn()'. But since the minimum wait time is
initialized to zero, there are corner cases against the original
objective.
For example, if we have several schemes for one target, and if the wait
time of the first scheme is zero, the minimum wait time will set zero,
which means 'kdamond_fn()' should wake up to apply this scheme.
However, in the following scheme, wait time can be set to non-zero.
Thus, the mininum wait time will be set to non-zero, which can cause
sleeping this interval for 'kdamon_fn()' due to one deactivated last
scheme.
This commit prevents making DAMON monitoring inactive state due to other
deactivated schemes.
Link: https://lkml.kernel.org/r/20220330105302.32114-1-tome01@ajou.ac.kr
Signed-off-by: Jonghyeon Kim <tome01@ajou.ac.kr>
Reviewed-by: SeongJae Park <sj@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-04-01 18:28:57 +00:00
|
|
|
bool init_wait_time = false;
|
mm/damon/schemes: activate schemes based on a watermarks mechanism
DAMON-based operation schemes need to be manually turned on and off. In
some use cases, however, the condition for turning a scheme on and off
would depend on the system's situation. For example, schemes for
proactive pages reclamation would need to be turned on when some memory
pressure is detected, and turned off when the system has enough free
memory.
For easier control of schemes activation based on the system situation,
this introduces a watermarks-based mechanism. The client can describe
the watermark metric (e.g., amount of free memory in the system),
watermark check interval, and three watermarks, namely high, mid, and
low. If the scheme is deactivated, it only gets the metric and compare
that to the three watermarks for every check interval. If the metric is
higher than the high watermark, the scheme is deactivated. If the
metric is between the mid watermark and the low watermark, the scheme is
activated. If the metric is lower than the low watermark, the scheme is
deactivated again. This is to allow users fall back to traditional
page-granularity mechanisms.
Link: https://lkml.kernel.org/r/20211019150731.16699-12-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:47:47 +00:00
|
|
|
|
|
|
|
while (!kdamond_need_stop(ctx)) {
|
|
|
|
damon_for_each_scheme(s, ctx) {
|
|
|
|
wait_time = damos_wmark_wait_us(s);
|
mm/damon: prevent activated scheme from sleeping by deactivated schemes
In the DAMON, the minimum wait time of the schemes decides whether the
kernel wakes up 'kdamon_fn()'. But since the minimum wait time is
initialized to zero, there are corner cases against the original
objective.
For example, if we have several schemes for one target, and if the wait
time of the first scheme is zero, the minimum wait time will set zero,
which means 'kdamond_fn()' should wake up to apply this scheme.
However, in the following scheme, wait time can be set to non-zero.
Thus, the mininum wait time will be set to non-zero, which can cause
sleeping this interval for 'kdamon_fn()' due to one deactivated last
scheme.
This commit prevents making DAMON monitoring inactive state due to other
deactivated schemes.
Link: https://lkml.kernel.org/r/20220330105302.32114-1-tome01@ajou.ac.kr
Signed-off-by: Jonghyeon Kim <tome01@ajou.ac.kr>
Reviewed-by: SeongJae Park <sj@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-04-01 18:28:57 +00:00
|
|
|
if (!init_wait_time || wait_time < min_wait_time) {
|
|
|
|
init_wait_time = true;
|
mm/damon/schemes: activate schemes based on a watermarks mechanism
DAMON-based operation schemes need to be manually turned on and off. In
some use cases, however, the condition for turning a scheme on and off
would depend on the system's situation. For example, schemes for
proactive pages reclamation would need to be turned on when some memory
pressure is detected, and turned off when the system has enough free
memory.
For easier control of schemes activation based on the system situation,
this introduces a watermarks-based mechanism. The client can describe
the watermark metric (e.g., amount of free memory in the system),
watermark check interval, and three watermarks, namely high, mid, and
low. If the scheme is deactivated, it only gets the metric and compare
that to the three watermarks for every check interval. If the metric is
higher than the high watermark, the scheme is deactivated. If the
metric is between the mid watermark and the low watermark, the scheme is
activated. If the metric is lower than the low watermark, the scheme is
deactivated again. This is to allow users fall back to traditional
page-granularity mechanisms.
Link: https://lkml.kernel.org/r/20211019150731.16699-12-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:47:47 +00:00
|
|
|
min_wait_time = wait_time;
|
mm/damon: prevent activated scheme from sleeping by deactivated schemes
In the DAMON, the minimum wait time of the schemes decides whether the
kernel wakes up 'kdamon_fn()'. But since the minimum wait time is
initialized to zero, there are corner cases against the original
objective.
For example, if we have several schemes for one target, and if the wait
time of the first scheme is zero, the minimum wait time will set zero,
which means 'kdamond_fn()' should wake up to apply this scheme.
However, in the following scheme, wait time can be set to non-zero.
Thus, the mininum wait time will be set to non-zero, which can cause
sleeping this interval for 'kdamon_fn()' due to one deactivated last
scheme.
This commit prevents making DAMON monitoring inactive state due to other
deactivated schemes.
Link: https://lkml.kernel.org/r/20220330105302.32114-1-tome01@ajou.ac.kr
Signed-off-by: Jonghyeon Kim <tome01@ajou.ac.kr>
Reviewed-by: SeongJae Park <sj@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-04-01 18:28:57 +00:00
|
|
|
}
|
mm/damon/schemes: activate schemes based on a watermarks mechanism
DAMON-based operation schemes need to be manually turned on and off. In
some use cases, however, the condition for turning a scheme on and off
would depend on the system's situation. For example, schemes for
proactive pages reclamation would need to be turned on when some memory
pressure is detected, and turned off when the system has enough free
memory.
For easier control of schemes activation based on the system situation,
this introduces a watermarks-based mechanism. The client can describe
the watermark metric (e.g., amount of free memory in the system),
watermark check interval, and three watermarks, namely high, mid, and
low. If the scheme is deactivated, it only gets the metric and compare
that to the three watermarks for every check interval. If the metric is
higher than the high watermark, the scheme is deactivated. If the
metric is between the mid watermark and the low watermark, the scheme is
activated. If the metric is lower than the low watermark, the scheme is
deactivated again. This is to allow users fall back to traditional
page-granularity mechanisms.
Link: https://lkml.kernel.org/r/20211019150731.16699-12-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:47:47 +00:00
|
|
|
}
|
|
|
|
if (!min_wait_time)
|
|
|
|
return 0;
|
|
|
|
|
|
|
|
kdamond_usleep(min_wait_time);
|
mm/damon/core: add a new callback for watermarks checks
Patch series "mm/damon: Support online tuning".
Effects of DAMON and DAMON-based Operation Schemes highly depends on the
configurations. Wrong configurations could even result in unexpected
efficiency degradations. For finding a best configuration, repeating
incremental configuration changes and results measurements, in other
words, online tuning, could be helpful.
Nevertheless, DAMON kernel API supports only restrictive online tuning.
Worse yet, the sysfs-based DAMON user interface doesn't support online
tuning at all. DAMON_RECLAIM also doesn't support online tuning.
This patchset makes the DAMON kernel API, DAMON sysfs interface, and
DAMON_RECLAIM supports online tuning.
Sequence of patches
-------------------
First two patches enhance DAMON online tuning for kernel API users.
Specifically, patch 1 let kernel API users to be able to do DAMON online
tuning without a restriction, and patch 2 makes error handling easier.
Following seven patches (patches 3-9) refactor code for better readability
and easier reuse of code fragments that will be useful for online tuning
support.
Patch 10 introduces DAMON callback based user request handling structure
for DAMON sysfs interface, and patch 11 enables DAMON online tuning via
DAMON sysfs interface. Documentation patch (patch 12) for usage of it
follows.
Patch 13 enables online tuning of DAMON_RECLAIM and finally patch 14
documents the DAMON_RECLAIM online tuning usage.
This patch (of 14):
For updating input parameters for running DAMON contexts, DAMON kernel API
users can use the contexts' callbacks, as it is the safe place for context
internal data accesses. When the context has DAMON-based operation
schemes and all schemes are deactivated due to their watermarks, however,
DAMON does nothing but only watermarks checks. As a result, no callbacks
will be called back, and therefore the kernel API users cannot update the
input parameters including monitoring attributes, DAMON-based operation
schemes, and watermarks.
To let users easily update such DAMON input parameters in such a case,
this commit adds a new callback, 'after_wmarks_check()'. It will be
called after each watermarks check. Users can do the online input
parameters update in the callback even under the schemes deactivated case.
Link: https://lkml.kernel.org/r/20220429160606.127307-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2022-05-10 01:20:54 +00:00
|
|
|
|
|
|
|
if (ctx->callback.after_wmarks_check &&
|
|
|
|
ctx->callback.after_wmarks_check(ctx))
|
|
|
|
break;
|
mm/damon/schemes: activate schemes based on a watermarks mechanism
DAMON-based operation schemes need to be manually turned on and off. In
some use cases, however, the condition for turning a scheme on and off
would depend on the system's situation. For example, schemes for
proactive pages reclamation would need to be turned on when some memory
pressure is detected, and turned off when the system has enough free
memory.
For easier control of schemes activation based on the system situation,
this introduces a watermarks-based mechanism. The client can describe
the watermark metric (e.g., amount of free memory in the system),
watermark check interval, and three watermarks, namely high, mid, and
low. If the scheme is deactivated, it only gets the metric and compare
that to the three watermarks for every check interval. If the metric is
higher than the high watermark, the scheme is deactivated. If the
metric is between the mid watermark and the low watermark, the scheme is
activated. If the metric is lower than the low watermark, the scheme is
deactivated again. This is to allow users fall back to traditional
page-granularity mechanisms.
Link: https://lkml.kernel.org/r/20211019150731.16699-12-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:47:47 +00:00
|
|
|
}
|
|
|
|
return -EBUSY;
|
|
|
|
}
|
|
|
|
|
mm/damon/core: use number of passed access sampling as a timer
DAMON sleeps for sampling interval after each sampling, and check if the
aggregation interval and the ops update interval have passed using
ktime_get_coarse_ts64() and baseline timestamps for the intervals. That
design is for making the operations occur at deterministic timing
regardless of the time that spend for each work. However, it turned out
it is not that useful, and incur not-that-intuitive results.
After all, timer functions, and especially sleep functions that DAMON uses
to wait for specific timing, are not necessarily strictly accurate. It is
legal design, so no problem. However, depending on such inaccuracies, the
nr_accesses can be larger than aggregation interval divided by sampling
interval. For example, with the default setting (5 ms sampling interval
and 100 ms aggregation interval) we frequently show regions having
nr_accesses larger than 20. Also, if the execution of a DAMOS scheme
takes a long time, next aggregation could happen before enough number of
samples are collected. This is not what usual users would intuitively
expect.
Since access check sampling is the smallest unit work of DAMON, using the
number of passed sampling intervals as the DAMON-internal timer can easily
avoid these problems. That is, convert aggregation and ops update
intervals to numbers of sampling intervals that need to be passed before
those operations be executed, count the number of passed sampling
intervals, and invoke the operations as soon as the specific amount of
sampling intervals passed. Make the change.
Note that this could make a behavioral change to settings that using
intervals that not aligned by the sampling interval. For example, if the
sampling interval is 5 ms and the aggregation interval is 12 ms, DAMON
effectively uses 15 ms as its aggregation interval, because it checks
whether the aggregation interval after sleeping the sampling interval.
This change will make DAMON to effectively use 10 ms as aggregation
interval, since it uses 'aggregation interval / sampling interval *
sampling interval' as the effective aggregation interval, and we don't use
floating point types. Usual users would have used aligned intervals, so
this behavioral change is not expected to make any meaningful impact, so
just make this change.
Link: https://lkml.kernel.org/r/20230914021523.60649-1-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-14 02:15:23 +00:00
|
|
|
static void kdamond_init_intervals_sis(struct damon_ctx *ctx)
|
|
|
|
{
|
|
|
|
unsigned long sample_interval = ctx->attrs.sample_interval ?
|
|
|
|
ctx->attrs.sample_interval : 1;
|
mm/damon/core: implement scheme-specific apply interval
DAMON-based operation schemes are applied for every aggregation interval.
That was mainly because schemes were using nr_accesses, which be complete
to be used for every aggregation interval. However, the schemes are now
using nr_accesses_bp, which is updated for each sampling interval in a way
that reasonable to be used. Therefore, there is no reason to apply
schemes for each aggregation interval.
The unnecessary alignment with aggregation interval was also making some
use cases of DAMOS tricky. Quotas setting under long aggregation interval
is one such example. Suppose the aggregation interval is ten seconds, and
there is a scheme having CPU quota 100ms per 1s. The scheme will actually
uses 100ms per ten seconds, since it cannobe be applied before next
aggregation interval. The feature is working as intended, but the results
might not that intuitive for some users. This could be fixed by updating
the quota to 1s per 10s. But, in the case, the CPU usage of DAMOS could
look like spikes, and would actually make a bad effect to other
CPU-sensitive workloads.
Implement a dedicated timing interval for each DAMON-based operation
scheme, namely apply_interval. The interval will be sampling interval
aligned, and each scheme will be applied for its apply_interval. The
interval is set to 0 by default, and it means the scheme should use the
aggregation interval instead. This avoids old users getting any
behavioral difference.
Link: https://lkml.kernel.org/r/20230916020945.47296-5-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (Google) <rostedt@goodmis.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-16 02:09:40 +00:00
|
|
|
unsigned long apply_interval;
|
|
|
|
struct damos *scheme;
|
mm/damon/core: use number of passed access sampling as a timer
DAMON sleeps for sampling interval after each sampling, and check if the
aggregation interval and the ops update interval have passed using
ktime_get_coarse_ts64() and baseline timestamps for the intervals. That
design is for making the operations occur at deterministic timing
regardless of the time that spend for each work. However, it turned out
it is not that useful, and incur not-that-intuitive results.
After all, timer functions, and especially sleep functions that DAMON uses
to wait for specific timing, are not necessarily strictly accurate. It is
legal design, so no problem. However, depending on such inaccuracies, the
nr_accesses can be larger than aggregation interval divided by sampling
interval. For example, with the default setting (5 ms sampling interval
and 100 ms aggregation interval) we frequently show regions having
nr_accesses larger than 20. Also, if the execution of a DAMOS scheme
takes a long time, next aggregation could happen before enough number of
samples are collected. This is not what usual users would intuitively
expect.
Since access check sampling is the smallest unit work of DAMON, using the
number of passed sampling intervals as the DAMON-internal timer can easily
avoid these problems. That is, convert aggregation and ops update
intervals to numbers of sampling intervals that need to be passed before
those operations be executed, count the number of passed sampling
intervals, and invoke the operations as soon as the specific amount of
sampling intervals passed. Make the change.
Note that this could make a behavioral change to settings that using
intervals that not aligned by the sampling interval. For example, if the
sampling interval is 5 ms and the aggregation interval is 12 ms, DAMON
effectively uses 15 ms as its aggregation interval, because it checks
whether the aggregation interval after sleeping the sampling interval.
This change will make DAMON to effectively use 10 ms as aggregation
interval, since it uses 'aggregation interval / sampling interval *
sampling interval' as the effective aggregation interval, and we don't use
floating point types. Usual users would have used aligned intervals, so
this behavioral change is not expected to make any meaningful impact, so
just make this change.
Link: https://lkml.kernel.org/r/20230914021523.60649-1-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-14 02:15:23 +00:00
|
|
|
|
|
|
|
ctx->passed_sample_intervals = 0;
|
|
|
|
ctx->next_aggregation_sis = ctx->attrs.aggr_interval / sample_interval;
|
|
|
|
ctx->next_ops_update_sis = ctx->attrs.ops_update_interval /
|
|
|
|
sample_interval;
|
mm/damon/core: implement scheme-specific apply interval
DAMON-based operation schemes are applied for every aggregation interval.
That was mainly because schemes were using nr_accesses, which be complete
to be used for every aggregation interval. However, the schemes are now
using nr_accesses_bp, which is updated for each sampling interval in a way
that reasonable to be used. Therefore, there is no reason to apply
schemes for each aggregation interval.
The unnecessary alignment with aggregation interval was also making some
use cases of DAMOS tricky. Quotas setting under long aggregation interval
is one such example. Suppose the aggregation interval is ten seconds, and
there is a scheme having CPU quota 100ms per 1s. The scheme will actually
uses 100ms per ten seconds, since it cannobe be applied before next
aggregation interval. The feature is working as intended, but the results
might not that intuitive for some users. This could be fixed by updating
the quota to 1s per 10s. But, in the case, the CPU usage of DAMOS could
look like spikes, and would actually make a bad effect to other
CPU-sensitive workloads.
Implement a dedicated timing interval for each DAMON-based operation
scheme, namely apply_interval. The interval will be sampling interval
aligned, and each scheme will be applied for its apply_interval. The
interval is set to 0 by default, and it means the scheme should use the
aggregation interval instead. This avoids old users getting any
behavioral difference.
Link: https://lkml.kernel.org/r/20230916020945.47296-5-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (Google) <rostedt@goodmis.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-16 02:09:40 +00:00
|
|
|
|
|
|
|
damon_for_each_scheme(scheme, ctx) {
|
|
|
|
apply_interval = scheme->apply_interval_us ?
|
|
|
|
scheme->apply_interval_us : ctx->attrs.aggr_interval;
|
|
|
|
scheme->next_apply_sis = apply_interval / sample_interval;
|
|
|
|
}
|
mm/damon/core: use number of passed access sampling as a timer
DAMON sleeps for sampling interval after each sampling, and check if the
aggregation interval and the ops update interval have passed using
ktime_get_coarse_ts64() and baseline timestamps for the intervals. That
design is for making the operations occur at deterministic timing
regardless of the time that spend for each work. However, it turned out
it is not that useful, and incur not-that-intuitive results.
After all, timer functions, and especially sleep functions that DAMON uses
to wait for specific timing, are not necessarily strictly accurate. It is
legal design, so no problem. However, depending on such inaccuracies, the
nr_accesses can be larger than aggregation interval divided by sampling
interval. For example, with the default setting (5 ms sampling interval
and 100 ms aggregation interval) we frequently show regions having
nr_accesses larger than 20. Also, if the execution of a DAMOS scheme
takes a long time, next aggregation could happen before enough number of
samples are collected. This is not what usual users would intuitively
expect.
Since access check sampling is the smallest unit work of DAMON, using the
number of passed sampling intervals as the DAMON-internal timer can easily
avoid these problems. That is, convert aggregation and ops update
intervals to numbers of sampling intervals that need to be passed before
those operations be executed, count the number of passed sampling
intervals, and invoke the operations as soon as the specific amount of
sampling intervals passed. Make the change.
Note that this could make a behavioral change to settings that using
intervals that not aligned by the sampling interval. For example, if the
sampling interval is 5 ms and the aggregation interval is 12 ms, DAMON
effectively uses 15 ms as its aggregation interval, because it checks
whether the aggregation interval after sleeping the sampling interval.
This change will make DAMON to effectively use 10 ms as aggregation
interval, since it uses 'aggregation interval / sampling interval *
sampling interval' as the effective aggregation interval, and we don't use
floating point types. Usual users would have used aligned intervals, so
this behavioral change is not expected to make any meaningful impact, so
just make this change.
Link: https://lkml.kernel.org/r/20230914021523.60649-1-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-14 02:15:23 +00:00
|
|
|
}
|
|
|
|
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
/*
|
|
|
|
* The monitoring daemon that runs as a kernel thread
|
|
|
|
*/
|
|
|
|
static int kdamond_fn(void *data)
|
|
|
|
{
|
2022-04-29 21:37:00 +00:00
|
|
|
struct damon_ctx *ctx = data;
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
struct damon_target *t;
|
|
|
|
struct damon_region *r, *next;
|
2021-09-08 02:56:36 +00:00
|
|
|
unsigned int max_nr_accesses = 0;
|
|
|
|
unsigned long sz_limit = 0;
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
|
2021-11-05 20:46:12 +00:00
|
|
|
pr_debug("kdamond (%d) starts\n", current->pid);
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
|
mm/damon/core: use number of passed access sampling as a timer
DAMON sleeps for sampling interval after each sampling, and check if the
aggregation interval and the ops update interval have passed using
ktime_get_coarse_ts64() and baseline timestamps for the intervals. That
design is for making the operations occur at deterministic timing
regardless of the time that spend for each work. However, it turned out
it is not that useful, and incur not-that-intuitive results.
After all, timer functions, and especially sleep functions that DAMON uses
to wait for specific timing, are not necessarily strictly accurate. It is
legal design, so no problem. However, depending on such inaccuracies, the
nr_accesses can be larger than aggregation interval divided by sampling
interval. For example, with the default setting (5 ms sampling interval
and 100 ms aggregation interval) we frequently show regions having
nr_accesses larger than 20. Also, if the execution of a DAMOS scheme
takes a long time, next aggregation could happen before enough number of
samples are collected. This is not what usual users would intuitively
expect.
Since access check sampling is the smallest unit work of DAMON, using the
number of passed sampling intervals as the DAMON-internal timer can easily
avoid these problems. That is, convert aggregation and ops update
intervals to numbers of sampling intervals that need to be passed before
those operations be executed, count the number of passed sampling
intervals, and invoke the operations as soon as the specific amount of
sampling intervals passed. Make the change.
Note that this could make a behavioral change to settings that using
intervals that not aligned by the sampling interval. For example, if the
sampling interval is 5 ms and the aggregation interval is 12 ms, DAMON
effectively uses 15 ms as its aggregation interval, because it checks
whether the aggregation interval after sleeping the sampling interval.
This change will make DAMON to effectively use 10 ms as aggregation
interval, since it uses 'aggregation interval / sampling interval *
sampling interval' as the effective aggregation interval, and we don't use
floating point types. Usual users would have used aligned intervals, so
this behavioral change is not expected to make any meaningful impact, so
just make this change.
Link: https://lkml.kernel.org/r/20230914021523.60649-1-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-14 02:15:23 +00:00
|
|
|
kdamond_init_intervals_sis(ctx);
|
|
|
|
|
2022-03-22 21:48:46 +00:00
|
|
|
if (ctx->ops.init)
|
|
|
|
ctx->ops.init(ctx);
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
if (ctx->callback.before_start && ctx->callback.before_start(ctx))
|
2022-09-13 09:11:26 +00:00
|
|
|
goto done;
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
|
2021-09-08 02:56:36 +00:00
|
|
|
sz_limit = damon_region_sz_limit(ctx);
|
|
|
|
|
2022-09-13 09:11:26 +00:00
|
|
|
while (!kdamond_need_stop(ctx)) {
|
mm/damon/core: use number of passed access sampling as a timer
DAMON sleeps for sampling interval after each sampling, and check if the
aggregation interval and the ops update interval have passed using
ktime_get_coarse_ts64() and baseline timestamps for the intervals. That
design is for making the operations occur at deterministic timing
regardless of the time that spend for each work. However, it turned out
it is not that useful, and incur not-that-intuitive results.
After all, timer functions, and especially sleep functions that DAMON uses
to wait for specific timing, are not necessarily strictly accurate. It is
legal design, so no problem. However, depending on such inaccuracies, the
nr_accesses can be larger than aggregation interval divided by sampling
interval. For example, with the default setting (5 ms sampling interval
and 100 ms aggregation interval) we frequently show regions having
nr_accesses larger than 20. Also, if the execution of a DAMOS scheme
takes a long time, next aggregation could happen before enough number of
samples are collected. This is not what usual users would intuitively
expect.
Since access check sampling is the smallest unit work of DAMON, using the
number of passed sampling intervals as the DAMON-internal timer can easily
avoid these problems. That is, convert aggregation and ops update
intervals to numbers of sampling intervals that need to be passed before
those operations be executed, count the number of passed sampling
intervals, and invoke the operations as soon as the specific amount of
sampling intervals passed. Make the change.
Note that this could make a behavioral change to settings that using
intervals that not aligned by the sampling interval. For example, if the
sampling interval is 5 ms and the aggregation interval is 12 ms, DAMON
effectively uses 15 ms as its aggregation interval, because it checks
whether the aggregation interval after sleeping the sampling interval.
This change will make DAMON to effectively use 10 ms as aggregation
interval, since it uses 'aggregation interval / sampling interval *
sampling interval' as the effective aggregation interval, and we don't use
floating point types. Usual users would have used aligned intervals, so
this behavioral change is not expected to make any meaningful impact, so
just make this change.
Link: https://lkml.kernel.org/r/20230914021523.60649-1-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-14 02:15:23 +00:00
|
|
|
/*
|
|
|
|
* ctx->attrs and ctx->next_{aggregation,ops_update}_sis could
|
|
|
|
* be changed from after_wmarks_check() or after_aggregation()
|
|
|
|
* callbacks. Read the values here, and use those for this
|
|
|
|
* iteration. That is, damon_set_attrs() updated new values
|
|
|
|
* are respected from next iteration.
|
|
|
|
*/
|
|
|
|
unsigned long next_aggregation_sis = ctx->next_aggregation_sis;
|
|
|
|
unsigned long next_ops_update_sis = ctx->next_ops_update_sis;
|
|
|
|
unsigned long sample_interval = ctx->attrs.sample_interval;
|
|
|
|
|
2022-09-13 09:11:26 +00:00
|
|
|
if (kdamond_wait_activation(ctx))
|
|
|
|
break;
|
mm/damon/schemes: activate schemes based on a watermarks mechanism
DAMON-based operation schemes need to be manually turned on and off. In
some use cases, however, the condition for turning a scheme on and off
would depend on the system's situation. For example, schemes for
proactive pages reclamation would need to be turned on when some memory
pressure is detected, and turned off when the system has enough free
memory.
For easier control of schemes activation based on the system situation,
this introduces a watermarks-based mechanism. The client can describe
the watermark metric (e.g., amount of free memory in the system),
watermark check interval, and three watermarks, namely high, mid, and
low. If the scheme is deactivated, it only gets the metric and compare
that to the three watermarks for every check interval. If the metric is
higher than the high watermark, the scheme is deactivated. If the
metric is between the mid watermark and the low watermark, the scheme is
activated. If the metric is lower than the low watermark, the scheme is
deactivated again. This is to allow users fall back to traditional
page-granularity mechanisms.
Link: https://lkml.kernel.org/r/20211019150731.16699-12-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Leonard Foerster <foersleo@amazon.de>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Shakeel Butt <shakeelb@google.com>
Cc: Shuah Khan <shuah@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-11-05 20:47:47 +00:00
|
|
|
|
2022-03-22 21:48:46 +00:00
|
|
|
if (ctx->ops.prepare_access_checks)
|
|
|
|
ctx->ops.prepare_access_checks(ctx);
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
if (ctx->callback.after_sampling &&
|
2022-09-13 09:11:26 +00:00
|
|
|
ctx->callback.after_sampling(ctx))
|
|
|
|
break;
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
|
mm/damon/core: use number of passed access sampling as a timer
DAMON sleeps for sampling interval after each sampling, and check if the
aggregation interval and the ops update interval have passed using
ktime_get_coarse_ts64() and baseline timestamps for the intervals. That
design is for making the operations occur at deterministic timing
regardless of the time that spend for each work. However, it turned out
it is not that useful, and incur not-that-intuitive results.
After all, timer functions, and especially sleep functions that DAMON uses
to wait for specific timing, are not necessarily strictly accurate. It is
legal design, so no problem. However, depending on such inaccuracies, the
nr_accesses can be larger than aggregation interval divided by sampling
interval. For example, with the default setting (5 ms sampling interval
and 100 ms aggregation interval) we frequently show regions having
nr_accesses larger than 20. Also, if the execution of a DAMOS scheme
takes a long time, next aggregation could happen before enough number of
samples are collected. This is not what usual users would intuitively
expect.
Since access check sampling is the smallest unit work of DAMON, using the
number of passed sampling intervals as the DAMON-internal timer can easily
avoid these problems. That is, convert aggregation and ops update
intervals to numbers of sampling intervals that need to be passed before
those operations be executed, count the number of passed sampling
intervals, and invoke the operations as soon as the specific amount of
sampling intervals passed. Make the change.
Note that this could make a behavioral change to settings that using
intervals that not aligned by the sampling interval. For example, if the
sampling interval is 5 ms and the aggregation interval is 12 ms, DAMON
effectively uses 15 ms as its aggregation interval, because it checks
whether the aggregation interval after sleeping the sampling interval.
This change will make DAMON to effectively use 10 ms as aggregation
interval, since it uses 'aggregation interval / sampling interval *
sampling interval' as the effective aggregation interval, and we don't use
floating point types. Usual users would have used aligned intervals, so
this behavioral change is not expected to make any meaningful impact, so
just make this change.
Link: https://lkml.kernel.org/r/20230914021523.60649-1-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-14 02:15:23 +00:00
|
|
|
kdamond_usleep(sample_interval);
|
|
|
|
ctx->passed_sample_intervals++;
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
|
2022-03-22 21:48:46 +00:00
|
|
|
if (ctx->ops.check_accesses)
|
|
|
|
max_nr_accesses = ctx->ops.check_accesses(ctx);
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
|
mm/damon/core: use number of passed access sampling as a timer
DAMON sleeps for sampling interval after each sampling, and check if the
aggregation interval and the ops update interval have passed using
ktime_get_coarse_ts64() and baseline timestamps for the intervals. That
design is for making the operations occur at deterministic timing
regardless of the time that spend for each work. However, it turned out
it is not that useful, and incur not-that-intuitive results.
After all, timer functions, and especially sleep functions that DAMON uses
to wait for specific timing, are not necessarily strictly accurate. It is
legal design, so no problem. However, depending on such inaccuracies, the
nr_accesses can be larger than aggregation interval divided by sampling
interval. For example, with the default setting (5 ms sampling interval
and 100 ms aggregation interval) we frequently show regions having
nr_accesses larger than 20. Also, if the execution of a DAMOS scheme
takes a long time, next aggregation could happen before enough number of
samples are collected. This is not what usual users would intuitively
expect.
Since access check sampling is the smallest unit work of DAMON, using the
number of passed sampling intervals as the DAMON-internal timer can easily
avoid these problems. That is, convert aggregation and ops update
intervals to numbers of sampling intervals that need to be passed before
those operations be executed, count the number of passed sampling
intervals, and invoke the operations as soon as the specific amount of
sampling intervals passed. Make the change.
Note that this could make a behavioral change to settings that using
intervals that not aligned by the sampling interval. For example, if the
sampling interval is 5 ms and the aggregation interval is 12 ms, DAMON
effectively uses 15 ms as its aggregation interval, because it checks
whether the aggregation interval after sleeping the sampling interval.
This change will make DAMON to effectively use 10 ms as aggregation
interval, since it uses 'aggregation interval / sampling interval *
sampling interval' as the effective aggregation interval, and we don't use
floating point types. Usual users would have used aligned intervals, so
this behavioral change is not expected to make any meaningful impact, so
just make this change.
Link: https://lkml.kernel.org/r/20230914021523.60649-1-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-14 02:15:23 +00:00
|
|
|
if (ctx->passed_sample_intervals == next_aggregation_sis) {
|
2021-09-08 02:56:36 +00:00
|
|
|
kdamond_merge_regions(ctx,
|
|
|
|
max_nr_accesses / 10,
|
|
|
|
sz_limit);
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
if (ctx->callback.after_aggregation &&
|
2022-09-13 09:11:26 +00:00
|
|
|
ctx->callback.after_aggregation(ctx))
|
|
|
|
break;
|
mm/damon/core: implement scheme-specific apply interval
DAMON-based operation schemes are applied for every aggregation interval.
That was mainly because schemes were using nr_accesses, which be complete
to be used for every aggregation interval. However, the schemes are now
using nr_accesses_bp, which is updated for each sampling interval in a way
that reasonable to be used. Therefore, there is no reason to apply
schemes for each aggregation interval.
The unnecessary alignment with aggregation interval was also making some
use cases of DAMOS tricky. Quotas setting under long aggregation interval
is one such example. Suppose the aggregation interval is ten seconds, and
there is a scheme having CPU quota 100ms per 1s. The scheme will actually
uses 100ms per ten seconds, since it cannobe be applied before next
aggregation interval. The feature is working as intended, but the results
might not that intuitive for some users. This could be fixed by updating
the quota to 1s per 10s. But, in the case, the CPU usage of DAMOS could
look like spikes, and would actually make a bad effect to other
CPU-sensitive workloads.
Implement a dedicated timing interval for each DAMON-based operation
scheme, namely apply_interval. The interval will be sampling interval
aligned, and each scheme will be applied for its apply_interval. The
interval is set to 0 by default, and it means the scheme should use the
aggregation interval instead. This avoids old users getting any
behavioral difference.
Link: https://lkml.kernel.org/r/20230916020945.47296-5-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (Google) <rostedt@goodmis.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-16 02:09:40 +00:00
|
|
|
}
|
|
|
|
|
|
|
|
/*
|
|
|
|
* do kdamond_apply_schemes() after kdamond_merge_regions() if
|
|
|
|
* possible, to reduce overhead
|
|
|
|
*/
|
|
|
|
if (!list_empty(&ctx->schemes))
|
|
|
|
kdamond_apply_schemes(ctx);
|
|
|
|
|
|
|
|
sample_interval = ctx->attrs.sample_interval ?
|
|
|
|
ctx->attrs.sample_interval : 1;
|
|
|
|
if (ctx->passed_sample_intervals == next_aggregation_sis) {
|
|
|
|
ctx->next_aggregation_sis = next_aggregation_sis +
|
|
|
|
ctx->attrs.aggr_interval / sample_interval;
|
|
|
|
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
kdamond_reset_aggregated(ctx);
|
2021-09-08 02:56:36 +00:00
|
|
|
kdamond_split_regions(ctx);
|
2022-03-22 21:48:46 +00:00
|
|
|
if (ctx->ops.reset_aggregated)
|
|
|
|
ctx->ops.reset_aggregated(ctx);
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
}
|
|
|
|
|
mm/damon/core: use number of passed access sampling as a timer
DAMON sleeps for sampling interval after each sampling, and check if the
aggregation interval and the ops update interval have passed using
ktime_get_coarse_ts64() and baseline timestamps for the intervals. That
design is for making the operations occur at deterministic timing
regardless of the time that spend for each work. However, it turned out
it is not that useful, and incur not-that-intuitive results.
After all, timer functions, and especially sleep functions that DAMON uses
to wait for specific timing, are not necessarily strictly accurate. It is
legal design, so no problem. However, depending on such inaccuracies, the
nr_accesses can be larger than aggregation interval divided by sampling
interval. For example, with the default setting (5 ms sampling interval
and 100 ms aggregation interval) we frequently show regions having
nr_accesses larger than 20. Also, if the execution of a DAMOS scheme
takes a long time, next aggregation could happen before enough number of
samples are collected. This is not what usual users would intuitively
expect.
Since access check sampling is the smallest unit work of DAMON, using the
number of passed sampling intervals as the DAMON-internal timer can easily
avoid these problems. That is, convert aggregation and ops update
intervals to numbers of sampling intervals that need to be passed before
those operations be executed, count the number of passed sampling
intervals, and invoke the operations as soon as the specific amount of
sampling intervals passed. Make the change.
Note that this could make a behavioral change to settings that using
intervals that not aligned by the sampling interval. For example, if the
sampling interval is 5 ms and the aggregation interval is 12 ms, DAMON
effectively uses 15 ms as its aggregation interval, because it checks
whether the aggregation interval after sleeping the sampling interval.
This change will make DAMON to effectively use 10 ms as aggregation
interval, since it uses 'aggregation interval / sampling interval *
sampling interval' as the effective aggregation interval, and we don't use
floating point types. Usual users would have used aligned intervals, so
this behavioral change is not expected to make any meaningful impact, so
just make this change.
Link: https://lkml.kernel.org/r/20230914021523.60649-1-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-14 02:15:23 +00:00
|
|
|
if (ctx->passed_sample_intervals == next_ops_update_sis) {
|
|
|
|
ctx->next_ops_update_sis = next_ops_update_sis +
|
|
|
|
ctx->attrs.ops_update_interval /
|
|
|
|
sample_interval;
|
2022-03-22 21:48:46 +00:00
|
|
|
if (ctx->ops.update)
|
|
|
|
ctx->ops.update(ctx);
|
2021-09-08 02:56:36 +00:00
|
|
|
sz_limit = damon_region_sz_limit(ctx);
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
}
|
|
|
|
}
|
2022-09-13 09:11:26 +00:00
|
|
|
done:
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
damon_for_each_target(t, ctx) {
|
|
|
|
damon_for_each_region_safe(r, next, t)
|
2021-09-08 02:56:36 +00:00
|
|
|
damon_destroy_region(r, t);
|
mm/damon/core: implement region-based sampling
To avoid the unbounded increase of the overhead, DAMON groups adjacent
pages that are assumed to have the same access frequencies into a
region. As long as the assumption (pages in a region have the same
access frequencies) is kept, only one page in the region is required to
be checked. Thus, for each ``sampling interval``,
1. the 'prepare_access_checks' primitive picks one page in each region,
2. waits for one ``sampling interval``,
3. checks whether the page is accessed meanwhile, and
4. increases the access count of the region if so.
Therefore, the monitoring overhead is controllable by adjusting the
number of regions. DAMON allows both the underlying primitives and user
callbacks to adjust regions for the trade-off. In other words, this
commit makes DAMON to use not only time-based sampling but also
space-based sampling.
This scheme, however, cannot preserve the quality of the output if the
assumption is not guaranteed. Next commit will address this problem.
Link: https://lkml.kernel.org/r/20210716081449.22187-3-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Rientjes <rientjes@google.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Joe Perches <joe@perches.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Marco Elver <elver@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Vlastimil Babka <vbabka@suse.cz>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:32 +00:00
|
|
|
}
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
|
2021-11-05 20:48:22 +00:00
|
|
|
if (ctx->callback.before_terminate)
|
|
|
|
ctx->callback.before_terminate(ctx);
|
2022-03-22 21:48:46 +00:00
|
|
|
if (ctx->ops.cleanup)
|
|
|
|
ctx->ops.cleanup(ctx);
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
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2021-11-05 20:46:12 +00:00
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pr_debug("kdamond (%d) finishes\n", current->pid);
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mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
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mutex_lock(&ctx->kdamond_lock);
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ctx->kdamond = NULL;
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mutex_unlock(&ctx->kdamond_lock);
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mutex_lock(&damon_lock);
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nr_running_ctxs--;
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mm/damon/core: allow non-exclusive DAMON start/stop
Patch series "Introduce DAMON sysfs interface", v3.
Introduction
============
DAMON's debugfs-based user interface (DAMON_DBGFS) served very well, so
far. However, it unnecessarily depends on debugfs, while DAMON is not
aimed to be used for only debugging. Also, the interface receives
multiple values via one file. For example, schemes file receives 18
values. As a result, it is inefficient, hard to be used, and difficult to
be extended. Especially, keeping backward compatibility of user space
tools is getting only challenging. It would be better to implement
another reliable and flexible interface and deprecate DAMON_DBGFS in long
term.
For the reason, this patchset introduces a sysfs-based new user interface
of DAMON. The idea of the new interface is, using directory hierarchies
and having one dedicated file for each value. For a short example, users
can do the virtual address monitoring via the interface as below:
# cd /sys/kernel/mm/damon/admin/
# echo 1 > kdamonds/nr_kdamonds
# echo 1 > kdamonds/0/contexts/nr_contexts
# echo vaddr > kdamonds/0/contexts/0/operations
# echo 1 > kdamonds/0/contexts/0/targets/nr_targets
# echo $(pidof <workload>) > kdamonds/0/contexts/0/targets/0/pid_target
# echo on > kdamonds/0/state
A brief representation of the files hierarchy of DAMON sysfs interface is
as below. Childs are represented with indentation, directories are having
'/' suffix, and files in each directory are separated by comma.
/sys/kernel/mm/damon/admin
│ kdamonds/nr_kdamonds
│ │ 0/state,pid
│ │ │ contexts/nr_contexts
│ │ │ │ 0/operations
│ │ │ │ │ monitoring_attrs/
│ │ │ │ │ │ intervals/sample_us,aggr_us,update_us
│ │ │ │ │ │ nr_regions/min,max
│ │ │ │ │ targets/nr_targets
│ │ │ │ │ │ 0/pid_target
│ │ │ │ │ │ │ regions/nr_regions
│ │ │ │ │ │ │ │ 0/start,end
│ │ │ │ │ │ │ │ ...
│ │ │ │ │ │ ...
│ │ │ │ │ schemes/nr_schemes
│ │ │ │ │ │ 0/action
│ │ │ │ │ │ │ access_pattern/
│ │ │ │ │ │ │ │ sz/min,max
│ │ │ │ │ │ │ │ nr_accesses/min,max
│ │ │ │ │ │ │ │ age/min,max
│ │ │ │ │ │ │ quotas/ms,bytes,reset_interval_ms
│ │ │ │ │ │ │ │ weights/sz_permil,nr_accesses_permil,age_permil
│ │ │ │ │ │ │ watermarks/metric,interval_us,high,mid,low
│ │ │ │ │ │ │ stats/nr_tried,sz_tried,nr_applied,sz_applied,qt_exceeds
│ │ │ │ │ │ ...
│ │ │ │ ...
│ │ ...
Detailed usage of the files will be described in the final Documentation
patch of this patchset.
Main Difference Between DAMON_DBGFS and DAMON_SYSFS
---------------------------------------------------
At the moment, DAMON_DBGFS and DAMON_SYSFS provides same features. One
important difference between them is their exclusiveness. DAMON_DBGFS
works in an exclusive manner, so that no DAMON worker thread (kdamond) in
the system can run concurrently and interfere somehow. For the reason,
DAMON_DBGFS asks users to construct all monitoring contexts and start them
at once. It's not a big problem but makes the operation a little bit
complex and unflexible.
For more flexible usage, DAMON_SYSFS moves the responsibility of
preventing any possible interference to the admins and work in a
non-exclusive manner. That is, users can configure and start contexts one
by one. Note that DAMON respects both exclusive groups and non-exclusive
groups of contexts, in a manner similar to that of reader-writer locks.
That is, if any exclusive monitoring contexts (e.g., contexts that started
via DAMON_DBGFS) are running, DAMON_SYSFS does not start new contexts, and
vice versa.
Future Plan of DAMON_DBGFS Deprecation
======================================
Once this patchset is merged, DAMON_DBGFS development will be frozen.
That is, we will maintain it to work as is now so that no users will be
break. But, it will not be extended to provide any new feature of DAMON.
The support will be continued only until next LTS release. After that, we
will drop DAMON_DBGFS.
User-space Tooling Compatibility
--------------------------------
As DAMON_SYSFS provides all features of DAMON_DBGFS, all user space
tooling can move to DAMON_SYSFS. As we will continue supporting
DAMON_DBGFS until next LTS kernel release, user space tools would have
enough time to move to DAMON_SYSFS.
The official user space tool, damo[1], is already supporting both
DAMON_SYSFS and DAMON_DBGFS. Both correctness tests[2] and performance
tests[3] of DAMON using DAMON_SYSFS also passed.
[1] https://github.com/awslabs/damo
[2] https://github.com/awslabs/damon-tests/tree/master/corr
[3] https://github.com/awslabs/damon-tests/tree/master/perf
Sequence of Patches
===================
First two patches (patches 1-2) make core changes for DAMON_SYSFS. The
first one (patch 1) allows non-exclusive DAMON contexts so that
DAMON_SYSFS can work in non-exclusive mode, while the second one (patch 2)
adds size of DAMON enum types so that DAMON API users can safely iterate
the enums.
Third patch (patch 3) implements basic sysfs stub for virtual address
spaces monitoring. Note that this implements only sysfs files and DAMON
is not linked. Fourth patch (patch 4) links the DAMON_SYSFS to DAMON so
that users can control DAMON using the sysfs files.
Following six patches (patches 5-10) implements other DAMON features that
DAMON_DBGFS supports one by one (physical address space monitoring,
DAMON-based operation schemes, schemes quotas, schemes prioritization
weights, schemes watermarks, and schemes stats).
Following patch (patch 11) adds a simple selftest for DAMON_SYSFS, and the
final one (patch 12) documents DAMON_SYSFS.
This patch (of 13):
To avoid interference between DAMON contexts monitoring overlapping memory
regions, damon_start() works in an exclusive manner. That is,
damon_start() does nothing bug fails if any context that started by
another instance of the function is still running. This makes its usage a
little bit restrictive. However, admins could aware each DAMON usage and
address such interferences on their own in some cases.
This commit hence implements non-exclusive mode of the function and allows
the callers to select the mode. Note that the exclusive groups and
non-exclusive groups of contexts will respect each other in a manner
similar to that of reader-writer locks. Therefore, this commit will not
cause any behavioral change to the exclusive groups.
Link: https://lkml.kernel.org/r/20220228081314.5770-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20220228081314.5770-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: Shuah Khan <skhan@linuxfoundation.org>
Cc: David Rientjes <rientjes@google.com>
Cc: Xin Hao <xhao@linux.alibaba.com>
Cc: Greg Kroah-Hartman <gregkh@linuxfoundation.org>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2022-03-22 21:49:21 +00:00
|
|
|
if (!nr_running_ctxs && running_exclusive_ctxs)
|
|
|
|
running_exclusive_ctxs = false;
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
mutex_unlock(&damon_lock);
|
|
|
|
|
2021-11-05 20:46:09 +00:00
|
|
|
return 0;
|
mm: introduce Data Access MONitor (DAMON)
Patch series "Introduce Data Access MONitor (DAMON)", v34.
Introduction
============
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON called 'region based sampling' and 'adaptive
regions adjustment' (refer to 'mechanisms.rst' in the 11th patch of this
patchset for the detail) make it
- accurate (The monitored information is useful for DRAM level memory
management. It might not appropriate for Cache-level accuracy,
though.),
- light-weight (The monitoring overhead is low enough to be applied
online while making no impact on the performance of the target
workloads.), and
- scalable (the upper-bound of the instrumentation overhead is
controllable regardless of the size of target workloads.).
Using this framework, therefore, several memory management mechanisms such
as reclamation and THP can be optimized to aware real data access
patterns. Experimental access pattern aware memory management
optimization works that incurring high instrumentation overhead will be
able to have another try.
Though DAMON is for kernel subsystems, it can be easily exposed to the
user space by writing a DAMON-wrapper kernel subsystem. Then, user space
users who have some special workloads will be able to write personalized
tools or applications for deeper understanding and specialized
optimizations of their systems.
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
The userspace tool[1] is available, released under GPLv2, and actively
being maintained. I am also planning to implement another basic user
interface in perf[2]. Also, the basic test suite for DAMON is available
under GPLv2[3].
[1] https://github.com/awslabs/damo
[2] https://lore.kernel.org/linux-mm/20210107120729.22328-1-sjpark@amazon.com/
[3] https://github.com/awslabs/damon-tests
Long-term Plan
--------------
DAMON is a part of a project called Data Access-aware Operating System
(DAOS). As the name implies, I want to improve the performance and
efficiency of systems using fine-grained data access patterns. The
optimizations are for both kernel and user spaces. I will therefore
modify or create kernel subsystems, export some of those to user space and
implement user space library / tools. Below shows the layers and
components for the project.
---------------------------------------------------------------------------
Primitives: PTE Accessed bit, PG_idle, rmap, (Intel CMT), ...
Framework: DAMON
Features: DAMOS, virtual addr, physical addr, ...
Applications: DAMON-debugfs, (DARC), ...
^^^^^^^^^^^^^^^^^^^^^^^ KERNEL SPACE ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Raw Interface: debugfs, (sysfs), (damonfs), tracepoints, (sys_damon), ...
vvvvvvvvvvvvvvvvvvvvvvv USER SPACE vvvvvvvvvvvvvvvvvvvvvvvvvvvvvvvv
Library: (libdamon), ...
Tools: DAMO, (perf), ...
---------------------------------------------------------------------------
The components in parentheses or marked as '...' are not implemented yet
but in the future plan. IOW, those are the TODO tasks of DAOS project.
For more detail, please refer to the plans:
https://lore.kernel.org/linux-mm/20201202082731.24828-1-sjpark@amazon.com/
Evaluations
===========
We evaluated DAMON's overhead, monitoring quality and usefulness using 24
realistic workloads on my QEMU/KVM based virtual machine running a kernel
that v24 DAMON patchset is applied.
DAMON is lightweight. It increases system memory usage by 0.39% and slows
target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An
experimental DAMON-based operation scheme for THP, namely 'ethp', removes
76.15% of THP memory overheads while preserving 51.25% of THP speedup.
Another experimental DAMON-based 'proactive reclamation' implementation,
'prcl', reduces 93.38% of residential sets and 23.63% of system memory
footprint while incurring only 1.22% runtime overhead in the best case
(parsec3/freqmine).
NOTE that the experimental THP optimization and proactive reclamation are
not for production but only for proof of concepts.
Please refer to the official document[1] or "Documentation/admin-guide/mm:
Add a document for DAMON" patch in this patchset for detailed evaluation
setup and results.
[1] https://damonitor.github.io/doc/html/latest-damon/admin-guide/mm/damon/eval.html
Real-world User Story
=====================
In summary, DAMON has used on production systems and proved its usefulness.
DAMON as a profiler
-------------------
We analyzed characteristics of a large scale production systems of our
customers using DAMON. The systems utilize 70GB DRAM and 36 CPUs. From
this, we were able to find interesting things below.
There were obviously different access pattern under idle workload and
active workload. Under the idle workload, it accessed large memory
regions with low frequency, while the active workload accessed small
memory regions with high freuqnecy.
DAMON found a 7GB memory region that showing obviously high access
frequency under the active workload. We believe this is the
performance-effective working set and need to be protected.
There was a 4KB memory region that showing highest access frequency under
not only active but also idle workloads. We think this must be a hottest
code section like thing that should never be paged out.
For this analysis, DAMON used only 0.3-1% of single CPU time. Because we
used recording-based analysis, it consumed about 3-12 MB of disk space per
20 minutes. This is only small amount of disk space, but we can further
reduce the disk usage by using non-recording-based DAMON features. I'd
like to argue that only DAMON can do such detailed analysis (finding 4KB
highest region in 70GB memory) with the light overhead.
DAMON as a system optimization tool
-----------------------------------
We also found below potential performance problems on the systems and made
DAMON-based solutions.
The system doesn't want to make the workload suffer from the page
reclamation and thus it utilizes enough DRAM but no swap device. However,
we found the system is actively reclaiming file-backed pages, because the
system has intensive file IO. The file IO turned out to be not
performance critical for the workload, but the customer wanted to ensure
performance critical file-backed pages like code section to not mistakenly
be evicted.
Using direct IO should or `mlock()` would be a straightforward solution,
but modifying the user space code is not easy for the customer.
Alternatively, we could use DAMON-based operation scheme[1]. By using it,
we can ask DAMON to track access frequency of each region and make
'process_madvise(MADV_WILLNEED)[2]' call for regions having specific size
and access frequency for a time interval.
We also found the system is having high number of TLB misses. We tried
'always' THP enabled policy and it greatly reduced TLB misses, but the
page reclamation also been more frequent due to the THP internal
fragmentation caused memory bloat. We could try another DAMON-based
operation scheme that applies 'MADV_HUGEPAGE' to memory regions having
>=2MB size and high access frequency, while applying 'MADV_NOHUGEPAGE' to
regions having <2MB size and low access frequency.
We do not own the systems so we only reported the analysis results and
possible optimization solutions to the customers. The customers satisfied
about the analysis results and promised to try the optimization guides.
[1] https://lore.kernel.org/linux-mm/20201006123931.5847-1-sjpark@amazon.com/
[2] https://lore.kernel.org/linux-api/20200622192900.22757-4-minchan@kernel.org/
Comparison with Idle Page Tracking
==================================
Idle Page Tracking allows users to set and read idleness of pages using a
bitmap file which represents each page with each bit of the file. One
recommended usage of it is working set size detection. Users can do that
by
1. find PFN of each page for workloads in interest,
2. set all the pages as idle by doing writes to the bitmap file,
3. wait until the workload accesses its working set, and
4. read the idleness of the pages again and count pages became not idle.
NOTE: While Idle Page Tracking is for user space users, DAMON is primarily
designed for kernel subsystems though it can easily exposed to the user
space. Hence, this section only assumes such user space use of DAMON.
For what use cases Idle Page Tracking would be better?
------------------------------------------------------
1. Flexible usecases other than hotness monitoring.
Because Idle Page Tracking allows users to control the primitive (Page
idleness) by themselves, Idle Page Tracking users can do anything they
want. Meanwhile, DAMON is primarily designed to monitor the hotness of
each memory region. For this, DAMON asks users to provide sampling
interval and aggregation interval. For the reason, there could be some
use case that using Idle Page Tracking is simpler.
2. Physical memory monitoring.
Idle Page Tracking receives PFN range as input, so natively supports
physical memory monitoring.
DAMON is designed to be extensible for multiple address spaces and use
cases by implementing and using primitives for the given use case.
Therefore, by theory, DAMON has no limitation in the type of target
address space as long as primitives for the given address space exists.
However, the default primitives introduced by this patchset supports only
virtual address spaces.
Therefore, for physical memory monitoring, you should implement your own
primitives and use it, or simply use Idle Page Tracking.
Nonetheless, RFC patchsets[1] for the physical memory address space
primitives is already available. It also supports user memory same to
Idle Page Tracking.
[1] https://lore.kernel.org/linux-mm/20200831104730.28970-1-sjpark@amazon.com/
For what use cases DAMON is better?
-----------------------------------
1. Hotness Monitoring.
Idle Page Tracking let users know only if a page frame is accessed or not.
For hotness check, the user should write more code and use more memory.
DAMON do that by itself.
2. Low Monitoring Overhead
DAMON receives user's monitoring request with one step and then provide
the results. So, roughly speaking, DAMON require only O(1) user/kernel
context switches.
In case of Idle Page Tracking, however, because the interface receives
contiguous page frames, the number of user/kernel context switches
increases as the monitoring target becomes complex and huge. As a result,
the context switch overhead could be not negligible.
Moreover, DAMON is born to handle with the monitoring overhead. Because
the core mechanism is pure logical, Idle Page Tracking users might be able
to implement the mechanism on their own, but it would be time consuming
and the user/kernel context switching will still more frequent than that
of DAMON. Also, the kernel subsystems cannot use the logic in this case.
3. Page granularity working set size detection.
Until v22 of this patchset, this was categorized as the thing Idle Page
Tracking could do better, because DAMON basically maintains additional
metadata for each of the monitoring target regions. So, in the page
granularity working set size detection use case, DAMON would incur (number
of monitoring target pages * size of metadata) memory overhead. Size of
the single metadata item is about 54 bytes, so assuming 4KB pages, about
1.3% of monitoring target pages will be additionally used.
All essential metadata for Idle Page Tracking are embedded in 'struct
page' and page table entries. Therefore, in this use case, only one
counter variable for working set size accounting is required if Idle Page
Tracking is used.
There are more details to consider, but roughly speaking, this is true in
most cases.
However, the situation changed from v23. Now DAMON supports arbitrary
types of monitoring targets, which don't use the metadata. Using that,
DAMON can do the working set size detection with no additional space
overhead but less user-kernel context switch. A first draft for the
implementation of monitoring primitives for this usage is available in a
DAMON development tree[1]. An RFC patchset for it based on this patchset
will also be available soon.
Since v24, the arbitrary type support is dropped from this patchset
because this patchset doesn't introduce real use of the type. You can
still get it from the DAMON development tree[2], though.
[1] https://github.com/sjp38/linux/tree/damon/pgidle_hack
[2] https://github.com/sjp38/linux/tree/damon/master
4. More future usecases
While Idle Page Tracking has tight coupling with base primitives (PG_Idle
and page table Accessed bits), DAMON is designed to be extensible for many
use cases and address spaces. If you need some special address type or
want to use special h/w access check primitives, you can write your own
primitives for that and configure DAMON to use those. Therefore, if your
use case could be changed a lot in future, using DAMON could be better.
Can I use both Idle Page Tracking and DAMON?
--------------------------------------------
Yes, though using them concurrently for overlapping memory regions could
result in interference to each other. Nevertheless, such use case would
be rare or makes no sense at all. Even in the case, the noise would bot
be really significant. So, you can choose whatever you want depending on
the characteristics of your use cases.
More Information
================
We prepared a showcase web site[1] that you can get more information.
There are
- the official documentations[2],
- the heatmap format dynamic access pattern of various realistic workloads for
heap area[3], mmap()-ed area[4], and stack[5] area,
- the dynamic working set size distribution[6] and chronological working set
size changes[7], and
- the latest performance test results[8].
[1] https://damonitor.github.io/_index
[2] https://damonitor.github.io/doc/html/latest-damon
[3] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.0.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[5] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.2.png.html
[6] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[7] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
Baseline and Complete Git Trees
===============================
The patches are based on the latest -mm tree, specifically
v5.14-rc1-mmots-2021-07-15-18-47 of https://github.com/hnaz/linux-mm. You can
also clone the complete git tree:
$ git clone git://github.com/sjp38/linux -b damon/patches/v34
The web is also available:
https://github.com/sjp38/linux/releases/tag/damon/patches/v34
Development Trees
-----------------
There are a couple of trees for entire DAMON patchset series and features
for future release.
- For latest release: https://github.com/sjp38/linux/tree/damon/master
- For next release: https://github.com/sjp38/linux/tree/damon/next
Long-term Support Trees
-----------------------
For people who want to test DAMON but using LTS kernels, there are another
couple of trees based on two latest LTS kernels respectively and
containing the 'damon/master' backports.
- For v5.4.y: https://github.com/sjp38/linux/tree/damon/for-v5.4.y
- For v5.10.y: https://github.com/sjp38/linux/tree/damon/for-v5.10.y
Amazon Linux Kernel Trees
-------------------------
DAMON is also merged in two public Amazon Linux kernel trees that based on
v5.4.y[1] and v5.10.y[2].
[1] https://github.com/amazonlinux/linux/tree/amazon-5.4.y/master/mm/damon
[2] https://github.com/amazonlinux/linux/tree/amazon-5.10.y/master/mm/damon
Git Tree for Diff of Patches
============================
For easy review of diff between different versions of each patch, I
prepared a git tree containing all versions of the DAMON patchset series:
https://github.com/sjp38/damon-patches
You can clone it and use 'diff' for easy review of changes between
different versions of the patchset. For example:
$ git clone https://github.com/sjp38/damon-patches && cd damon-patches
$ diff -u damon/v33 damon/v34
Sequence Of Patches
===================
First three patches implement the core logics of DAMON. The 1st patch
introduces basic sampling based hotness monitoring for arbitrary types of
targets. Following two patches implement the core mechanisms for control
of overhead and accuracy, namely regions based sampling (patch 2) and
adaptive regions adjustment (patch 3).
Now the essential parts of DAMON is complete, but it cannot work unless
someone provides monitoring primitives for a specific use case. The
following two patches make it just work for virtual address spaces
monitoring. The 4th patch makes 'PG_idle' can be used by DAMON and the
5th patch implements the virtual memory address space specific monitoring
primitives using page table Accessed bits and the 'PG_idle' page flag.
Now DAMON just works for virtual address space monitoring via the kernel
space api. To let the user space users can use DAMON, following four
patches add interfaces for them. The 6th patch adds a tracepoint for
monitoring results. The 7th patch implements a DAMON application kernel
module, namely damon-dbgfs, that simply wraps DAMON and exposes DAMON
interface to the user space via the debugfs interface. The 8th patch
further exports pid of monitoring thread (kdamond) to user space for
easier cpu usage accounting, and the 9th patch makes the debugfs interface
to support multiple contexts.
Three patches for maintainability follows. The 10th patch adds
documentations for both the user space and the kernel space. The 11th
patch provides unit tests (based on the kunit) while the 12th patch adds
user space tests (based on the kselftest).
Finally, the last patch (13th) updates the MAINTAINERS file.
This patch (of 13):
DAMON is a data access monitoring framework for the Linux kernel. The
core mechanisms of DAMON make it
- accurate (the monitoring output is useful enough for DRAM level
performance-centric memory management; It might be inappropriate for
CPU cache levels, though),
- light-weight (the monitoring overhead is normally low enough to be
applied online), and
- scalable (the upper-bound of the overhead is in constant range
regardless of the size of target workloads).
Using this framework, hence, we can easily write efficient kernel space
data access monitoring applications. For example, the kernel's memory
management mechanisms can make advanced decisions using this.
Experimental data access aware optimization works that incurring high
access monitoring overhead could again be implemented on top of this.
Due to its simple and flexible interface, providing user space interface
would be also easy. Then, user space users who have some special
workloads can write personalized applications for better understanding and
optimizations of their workloads and systems.
===
Nevertheless, this commit is defining and implementing only basic access
check part without the overhead-accuracy handling core logic. The basic
access check is as below.
The output of DAMON says what memory regions are how frequently accessed
for a given duration. The resolution of the access frequency is
controlled by setting ``sampling interval`` and ``aggregation interval``.
In detail, DAMON checks access to each page per ``sampling interval`` and
aggregates the results. In other words, counts the number of the accesses
to each region. After each ``aggregation interval`` passes, DAMON calls
callback functions that previously registered by users so that users can
read the aggregated results and then clears the results. This can be
described in below simple pseudo-code::
init()
while monitoring_on:
for page in monitoring_target:
if accessed(page):
nr_accesses[page] += 1
if time() % aggregation_interval == 0:
for callback in user_registered_callbacks:
callback(monitoring_target, nr_accesses)
for page in monitoring_target:
nr_accesses[page] = 0
if time() % update_interval == 0:
update()
sleep(sampling interval)
The target regions constructed at the beginning of the monitoring and
updated after each ``regions_update_interval``, because the target regions
could be dynamically changed (e.g., mmap() or memory hotplug). The
monitoring overhead of this mechanism will arbitrarily increase as the
size of the target workload grows.
The basic monitoring primitives for actual access check and dynamic target
regions construction aren't in the core part of DAMON. Instead, it allows
users to implement their own primitives that are optimized for their use
case and configure DAMON to use those. In other words, users cannot use
current version of DAMON without some additional works.
Following commits will implement the core mechanisms for the
overhead-accuracy control and default primitives implementations.
Link: https://lkml.kernel.org/r/20210716081449.22187-1-sj38.park@gmail.com
Link: https://lkml.kernel.org/r/20210716081449.22187-2-sj38.park@gmail.com
Signed-off-by: SeongJae Park <sjpark@amazon.de>
Reviewed-by: Leonard Foerster <foersleo@amazon.de>
Reviewed-by: Fernand Sieber <sieberf@amazon.com>
Acked-by: Shakeel Butt <shakeelb@google.com>
Cc: Jonathan Cameron <Jonathan.Cameron@huawei.com>
Cc: Alexander Shishkin <alexander.shishkin@linux.intel.com>
Cc: Amit Shah <amit@kernel.org>
Cc: Benjamin Herrenschmidt <benh@kernel.crashing.org>
Cc: Jonathan Corbet <corbet@lwn.net>
Cc: David Hildenbrand <david@redhat.com>
Cc: David Woodhouse <dwmw@amazon.com>
Cc: Marco Elver <elver@google.com>
Cc: Fan Du <fan.du@intel.com>
Cc: Greg Kroah-Hartman <greg@kroah.com>
Cc: Greg Thelen <gthelen@google.com>
Cc: Joe Perches <joe@perches.com>
Cc: Mel Gorman <mgorman@suse.de>
Cc: Maximilian Heyne <mheyne@amazon.de>
Cc: Minchan Kim <minchan@kernel.org>
Cc: Ingo Molnar <mingo@redhat.com>
Cc: Namhyung Kim <namhyung@kernel.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rik van Riel <riel@surriel.com>
Cc: David Rientjes <rientjes@google.com>
Cc: Steven Rostedt (VMware) <rostedt@goodmis.org>
Cc: Shuah Khan <shuah@kernel.org>
Cc: Vlastimil Babka <vbabka@suse.cz>
Cc: Vladimir Davydov <vdavydov.dev@gmail.com>
Cc: Brendan Higgins <brendanhiggins@google.com>
Cc: Markus Boehme <markubo@amazon.de>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org>
2021-09-08 02:56:28 +00:00
|
|
|
}
|
2021-09-08 02:57:09 +00:00
|
|
|
|
2022-09-09 21:36:06 +00:00
|
|
|
/*
|
|
|
|
* struct damon_system_ram_region - System RAM resource address region of
|
|
|
|
* [@start, @end).
|
|
|
|
* @start: Start address of the region (inclusive).
|
|
|
|
* @end: End address of the region (exclusive).
|
|
|
|
*/
|
|
|
|
struct damon_system_ram_region {
|
|
|
|
unsigned long start;
|
|
|
|
unsigned long end;
|
|
|
|
};
|
|
|
|
|
|
|
|
static int walk_system_ram(struct resource *res, void *arg)
|
|
|
|
{
|
|
|
|
struct damon_system_ram_region *a = arg;
|
|
|
|
|
|
|
|
if (a->end - a->start < resource_size(res)) {
|
|
|
|
a->start = res->start;
|
|
|
|
a->end = res->end;
|
|
|
|
}
|
|
|
|
return 0;
|
|
|
|
}
|
|
|
|
|
|
|
|
/*
|
|
|
|
* Find biggest 'System RAM' resource and store its start and end address in
|
|
|
|
* @start and @end, respectively. If no System RAM is found, returns false.
|
|
|
|
*/
|
2022-09-20 16:53:22 +00:00
|
|
|
static bool damon_find_biggest_system_ram(unsigned long *start,
|
|
|
|
unsigned long *end)
|
2022-09-09 21:36:06 +00:00
|
|
|
|
|
|
|
{
|
|
|
|
struct damon_system_ram_region arg = {};
|
|
|
|
|
|
|
|
walk_system_ram_res(0, ULONG_MAX, &arg, walk_system_ram);
|
|
|
|
if (arg.end <= arg.start)
|
|
|
|
return false;
|
|
|
|
|
|
|
|
*start = arg.start;
|
|
|
|
*end = arg.end;
|
|
|
|
return true;
|
|
|
|
}
|
|
|
|
|
2022-09-20 16:53:22 +00:00
|
|
|
/**
|
|
|
|
* damon_set_region_biggest_system_ram_default() - Set the region of the given
|
|
|
|
* monitoring target as requested, or biggest 'System RAM'.
|
|
|
|
* @t: The monitoring target to set the region.
|
|
|
|
* @start: The pointer to the start address of the region.
|
|
|
|
* @end: The pointer to the end address of the region.
|
|
|
|
*
|
|
|
|
* This function sets the region of @t as requested by @start and @end. If the
|
|
|
|
* values of @start and @end are zero, however, this function finds the biggest
|
|
|
|
* 'System RAM' resource and sets the region to cover the resource. In the
|
|
|
|
* latter case, this function saves the start and end addresses of the resource
|
|
|
|
* in @start and @end, respectively.
|
|
|
|
*
|
|
|
|
* Return: 0 on success, negative error code otherwise.
|
|
|
|
*/
|
|
|
|
int damon_set_region_biggest_system_ram_default(struct damon_target *t,
|
|
|
|
unsigned long *start, unsigned long *end)
|
|
|
|
{
|
|
|
|
struct damon_addr_range addr_range;
|
|
|
|
|
|
|
|
if (*start > *end)
|
|
|
|
return -EINVAL;
|
|
|
|
|
|
|
|
if (!*start && !*end &&
|
|
|
|
!damon_find_biggest_system_ram(start, end))
|
|
|
|
return -EINVAL;
|
|
|
|
|
|
|
|
addr_range.start = *start;
|
|
|
|
addr_range.end = *end;
|
|
|
|
return damon_set_regions(t, &addr_range, 1);
|
|
|
|
}
|
|
|
|
|
mm/damon/core: implement a pseudo-moving sum function
For values that continuously change, moving average or sum are good ways
to provide fast updates while handling temporal and errorneous variability
of the value. For example, the access rate counter (nr_accesses) is
calculated as a sum of the number of positive sampled access check results
that collected during a discrete time window (aggregation interval), and
hence it handles temporal and errorneous access check results, but
provides the update only for every aggregation interval. Using a moving
sum method for that could allow providing the value for every sampling
interval. That could be useful for getting monitoring results snapshot or
running DAMOS in fine-grained timing.
However, supporting the moving sum for cases that number of samples in the
time window is arbirary could impose high overhead, since the number of
past values that it needs to keep could be too high. The nr_accesses
would also be one of the cases. To mitigate the overhead, implement a
pseudo-moving sum function that only provides an estimated pseudo-moving
sum. It assumes there was no error in last discrete time window and
subtract constant portion of last discrete time window sum.
Note that the function is not strictly implementing the moving sum, but it
keeps a property of moving sum, which makes the value same to the
dsicrete-window based sum for each time window-aligned timing. Hence,
people collecting the value in the old timings would show no difference.
Link: https://lkml.kernel.org/r/20230915025251.72816-4-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-15 02:52:46 +00:00
|
|
|
/*
|
|
|
|
* damon_moving_sum() - Calculate an inferred moving sum value.
|
|
|
|
* @mvsum: Inferred sum of the last @len_window values.
|
|
|
|
* @nomvsum: Non-moving sum of the last discrete @len_window window values.
|
|
|
|
* @len_window: The number of last values to take care of.
|
|
|
|
* @new_value: New value that will be added to the pseudo moving sum.
|
|
|
|
*
|
|
|
|
* Moving sum (moving average * window size) is good for handling noise, but
|
|
|
|
* the cost of keeping past values can be high for arbitrary window size. This
|
|
|
|
* function implements a lightweight pseudo moving sum function that doesn't
|
|
|
|
* keep the past window values.
|
|
|
|
*
|
|
|
|
* It simply assumes there was no noise in the past, and get the no-noise
|
|
|
|
* assumed past value to drop from @nomvsum and @len_window. @nomvsum is a
|
|
|
|
* non-moving sum of the last window. For example, if @len_window is 10 and we
|
|
|
|
* have 25 values, @nomvsum is the sum of the 11th to 20th values of the 25
|
|
|
|
* values. Hence, this function simply drops @nomvsum / @len_window from
|
|
|
|
* given @mvsum and add @new_value.
|
|
|
|
*
|
|
|
|
* For example, if @len_window is 10 and @nomvsum is 50, the last 10 values for
|
|
|
|
* the last window could be vary, e.g., 0, 10, 0, 10, 0, 10, 0, 0, 0, 20. For
|
|
|
|
* calculating next moving sum with a new value, we should drop 0 from 50 and
|
|
|
|
* add the new value. However, this function assumes it got value 5 for each
|
|
|
|
* of the last ten times. Based on the assumption, when the next value is
|
|
|
|
* measured, it drops the assumed past value, 5 from the current sum, and add
|
|
|
|
* the new value to get the updated pseduo-moving average.
|
|
|
|
*
|
|
|
|
* This means the value could have errors, but the errors will be disappeared
|
|
|
|
* for every @len_window aligned calls. For example, if @len_window is 10, the
|
|
|
|
* pseudo moving sum with 11th value to 19th value would have an error. But
|
|
|
|
* the sum with 20th value will not have the error.
|
|
|
|
*
|
|
|
|
* Return: Pseudo-moving average after getting the @new_value.
|
|
|
|
*/
|
2023-09-15 02:52:51 +00:00
|
|
|
static unsigned int damon_moving_sum(unsigned int mvsum, unsigned int nomvsum,
|
mm/damon/core: implement a pseudo-moving sum function
For values that continuously change, moving average or sum are good ways
to provide fast updates while handling temporal and errorneous variability
of the value. For example, the access rate counter (nr_accesses) is
calculated as a sum of the number of positive sampled access check results
that collected during a discrete time window (aggregation interval), and
hence it handles temporal and errorneous access check results, but
provides the update only for every aggregation interval. Using a moving
sum method for that could allow providing the value for every sampling
interval. That could be useful for getting monitoring results snapshot or
running DAMOS in fine-grained timing.
However, supporting the moving sum for cases that number of samples in the
time window is arbirary could impose high overhead, since the number of
past values that it needs to keep could be too high. The nr_accesses
would also be one of the cases. To mitigate the overhead, implement a
pseudo-moving sum function that only provides an estimated pseudo-moving
sum. It assumes there was no error in last discrete time window and
subtract constant portion of last discrete time window sum.
Note that the function is not strictly implementing the moving sum, but it
keeps a property of moving sum, which makes the value same to the
dsicrete-window based sum for each time window-aligned timing. Hence,
people collecting the value in the old timings would show no difference.
Link: https://lkml.kernel.org/r/20230915025251.72816-4-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-15 02:52:46 +00:00
|
|
|
unsigned int len_window, unsigned int new_value)
|
|
|
|
{
|
|
|
|
return mvsum - nomvsum / len_window + new_value;
|
|
|
|
}
|
|
|
|
|
mm/damon/core: define and use a dedicated function for region access rate update
Patch series "mm/damon: provide pseudo-moving sum based access rate".
DAMON checks the access to each region for every sampling interval,
increase the access rate counter of the region, namely nr_accesses, if the
access was made. For every aggregation interval, the counter is reset.
The counter is exposed to users to be used as a metric showing the
relative access rate (frequency) of each region. In other words, DAMON
provides access rate of each region in every aggregation interval. The
aggregation avoids temporal access pattern changes making things
confusing. However, this also makes a few DAMON-related operations to
unnecessarily need to be aligned to the aggregation interval. This can
restrict the flexibility of DAMON applications, especially when the
aggregation interval is huge.
To provide the monitoring results in finer-grained timing while keeping
handling of temporal access pattern change, this patchset implements a
pseudo-moving sum based access rate metric. It is pseudo-moving sum
because strict moving sum implementation would need to keep all values for
last time window, and that could incur high overhead of there could be
arbitrary number of values in a time window. Especially in case of the
nr_accesses, since the sampling interval and aggregation interval can
arbitrarily set and the past values should be maintained for every region,
it could be risky. The pseudo-moving sum assumes there were no temporal
access pattern change in last discrete time window to remove the needs for
keeping the list of the last time window values. As a result, it beocmes
not strict moving sum implementation, but provides a reasonable accuracy.
Also, it keeps an important property of the moving sum. That is, the
moving sum becomes same to discrete-window based sum at the time that
aligns to the time window. This means using the pseudo moving sum based
nr_accesses makes no change to users who shows the value for every
aggregation interval.
Patches Sequence
----------------
The sequence of the patches is as follows. The first four patches are for
preparation of the change. The first two (patches 1 and 2) implements a
helper function for nr_accesses update and eliminate corner case that
skips use of the function, respectively. Following two (patches 3 and 4)
respectively implement the pseudo-moving sum function and its simple unit
test case.
Two patches for making DAMON to use the pseudo-moving sum follow. The
fifthe one (patch 5) introduces a new field for representing the
pseudo-moving sum-based access rate of each region, and the sixth one
makes the new representation to actually updated with the pseudo-moving
sum function.
Last two patches (patches 7 and 8) makes followup fixes for skipping
unnecessary updates and marking the moving sum function as static,
respectively.
This patch (of 8):
Each DAMON operarions set is updating nr_accesses field of each
damon_region for each of their access check results, from the
check_accesses() callback. Directly accessing the field could make things
complex to manage and change in future. Define and use a dedicated
function for the purpose.
Link: https://lkml.kernel.org/r/20230915025251.72816-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20230915025251.72816-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-15 02:52:44 +00:00
|
|
|
/**
|
|
|
|
* damon_update_region_access_rate() - Update the access rate of a region.
|
|
|
|
* @r: The DAMON region to update for its access check result.
|
|
|
|
* @accessed: Whether the region has accessed during last sampling interval.
|
2023-09-15 02:52:49 +00:00
|
|
|
* @attrs: The damon_attrs of the DAMON context.
|
mm/damon/core: define and use a dedicated function for region access rate update
Patch series "mm/damon: provide pseudo-moving sum based access rate".
DAMON checks the access to each region for every sampling interval,
increase the access rate counter of the region, namely nr_accesses, if the
access was made. For every aggregation interval, the counter is reset.
The counter is exposed to users to be used as a metric showing the
relative access rate (frequency) of each region. In other words, DAMON
provides access rate of each region in every aggregation interval. The
aggregation avoids temporal access pattern changes making things
confusing. However, this also makes a few DAMON-related operations to
unnecessarily need to be aligned to the aggregation interval. This can
restrict the flexibility of DAMON applications, especially when the
aggregation interval is huge.
To provide the monitoring results in finer-grained timing while keeping
handling of temporal access pattern change, this patchset implements a
pseudo-moving sum based access rate metric. It is pseudo-moving sum
because strict moving sum implementation would need to keep all values for
last time window, and that could incur high overhead of there could be
arbitrary number of values in a time window. Especially in case of the
nr_accesses, since the sampling interval and aggregation interval can
arbitrarily set and the past values should be maintained for every region,
it could be risky. The pseudo-moving sum assumes there were no temporal
access pattern change in last discrete time window to remove the needs for
keeping the list of the last time window values. As a result, it beocmes
not strict moving sum implementation, but provides a reasonable accuracy.
Also, it keeps an important property of the moving sum. That is, the
moving sum becomes same to discrete-window based sum at the time that
aligns to the time window. This means using the pseudo moving sum based
nr_accesses makes no change to users who shows the value for every
aggregation interval.
Patches Sequence
----------------
The sequence of the patches is as follows. The first four patches are for
preparation of the change. The first two (patches 1 and 2) implements a
helper function for nr_accesses update and eliminate corner case that
skips use of the function, respectively. Following two (patches 3 and 4)
respectively implement the pseudo-moving sum function and its simple unit
test case.
Two patches for making DAMON to use the pseudo-moving sum follow. The
fifthe one (patch 5) introduces a new field for representing the
pseudo-moving sum-based access rate of each region, and the sixth one
makes the new representation to actually updated with the pseudo-moving
sum function.
Last two patches (patches 7 and 8) makes followup fixes for skipping
unnecessary updates and marking the moving sum function as static,
respectively.
This patch (of 8):
Each DAMON operarions set is updating nr_accesses field of each
damon_region for each of their access check results, from the
check_accesses() callback. Directly accessing the field could make things
complex to manage and change in future. Define and use a dedicated
function for the purpose.
Link: https://lkml.kernel.org/r/20230915025251.72816-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20230915025251.72816-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-15 02:52:44 +00:00
|
|
|
*
|
|
|
|
* Update the access rate of a region with the region's last sampling interval
|
|
|
|
* access check result.
|
|
|
|
*
|
|
|
|
* Usually this will be called by &damon_operations->check_accesses callback.
|
|
|
|
*/
|
2023-09-15 02:52:49 +00:00
|
|
|
void damon_update_region_access_rate(struct damon_region *r, bool accessed,
|
|
|
|
struct damon_attrs *attrs)
|
mm/damon/core: define and use a dedicated function for region access rate update
Patch series "mm/damon: provide pseudo-moving sum based access rate".
DAMON checks the access to each region for every sampling interval,
increase the access rate counter of the region, namely nr_accesses, if the
access was made. For every aggregation interval, the counter is reset.
The counter is exposed to users to be used as a metric showing the
relative access rate (frequency) of each region. In other words, DAMON
provides access rate of each region in every aggregation interval. The
aggregation avoids temporal access pattern changes making things
confusing. However, this also makes a few DAMON-related operations to
unnecessarily need to be aligned to the aggregation interval. This can
restrict the flexibility of DAMON applications, especially when the
aggregation interval is huge.
To provide the monitoring results in finer-grained timing while keeping
handling of temporal access pattern change, this patchset implements a
pseudo-moving sum based access rate metric. It is pseudo-moving sum
because strict moving sum implementation would need to keep all values for
last time window, and that could incur high overhead of there could be
arbitrary number of values in a time window. Especially in case of the
nr_accesses, since the sampling interval and aggregation interval can
arbitrarily set and the past values should be maintained for every region,
it could be risky. The pseudo-moving sum assumes there were no temporal
access pattern change in last discrete time window to remove the needs for
keeping the list of the last time window values. As a result, it beocmes
not strict moving sum implementation, but provides a reasonable accuracy.
Also, it keeps an important property of the moving sum. That is, the
moving sum becomes same to discrete-window based sum at the time that
aligns to the time window. This means using the pseudo moving sum based
nr_accesses makes no change to users who shows the value for every
aggregation interval.
Patches Sequence
----------------
The sequence of the patches is as follows. The first four patches are for
preparation of the change. The first two (patches 1 and 2) implements a
helper function for nr_accesses update and eliminate corner case that
skips use of the function, respectively. Following two (patches 3 and 4)
respectively implement the pseudo-moving sum function and its simple unit
test case.
Two patches for making DAMON to use the pseudo-moving sum follow. The
fifthe one (patch 5) introduces a new field for representing the
pseudo-moving sum-based access rate of each region, and the sixth one
makes the new representation to actually updated with the pseudo-moving
sum function.
Last two patches (patches 7 and 8) makes followup fixes for skipping
unnecessary updates and marking the moving sum function as static,
respectively.
This patch (of 8):
Each DAMON operarions set is updating nr_accesses field of each
damon_region for each of their access check results, from the
check_accesses() callback. Directly accessing the field could make things
complex to manage and change in future. Define and use a dedicated
function for the purpose.
Link: https://lkml.kernel.org/r/20230915025251.72816-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20230915025251.72816-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-15 02:52:44 +00:00
|
|
|
{
|
2023-09-15 02:52:49 +00:00
|
|
|
unsigned int len_window = 1;
|
|
|
|
|
|
|
|
/*
|
|
|
|
* sample_interval can be zero, but cannot be larger than
|
|
|
|
* aggr_interval, owing to validation of damon_set_attrs().
|
|
|
|
*/
|
|
|
|
if (attrs->sample_interval)
|
2023-10-19 19:49:24 +00:00
|
|
|
len_window = damon_max_nr_accesses(attrs);
|
2023-09-15 02:52:49 +00:00
|
|
|
r->nr_accesses_bp = damon_moving_sum(r->nr_accesses_bp,
|
|
|
|
r->last_nr_accesses * 10000, len_window,
|
|
|
|
accessed ? 10000 : 0);
|
|
|
|
|
mm/damon/core: define and use a dedicated function for region access rate update
Patch series "mm/damon: provide pseudo-moving sum based access rate".
DAMON checks the access to each region for every sampling interval,
increase the access rate counter of the region, namely nr_accesses, if the
access was made. For every aggregation interval, the counter is reset.
The counter is exposed to users to be used as a metric showing the
relative access rate (frequency) of each region. In other words, DAMON
provides access rate of each region in every aggregation interval. The
aggregation avoids temporal access pattern changes making things
confusing. However, this also makes a few DAMON-related operations to
unnecessarily need to be aligned to the aggregation interval. This can
restrict the flexibility of DAMON applications, especially when the
aggregation interval is huge.
To provide the monitoring results in finer-grained timing while keeping
handling of temporal access pattern change, this patchset implements a
pseudo-moving sum based access rate metric. It is pseudo-moving sum
because strict moving sum implementation would need to keep all values for
last time window, and that could incur high overhead of there could be
arbitrary number of values in a time window. Especially in case of the
nr_accesses, since the sampling interval and aggregation interval can
arbitrarily set and the past values should be maintained for every region,
it could be risky. The pseudo-moving sum assumes there were no temporal
access pattern change in last discrete time window to remove the needs for
keeping the list of the last time window values. As a result, it beocmes
not strict moving sum implementation, but provides a reasonable accuracy.
Also, it keeps an important property of the moving sum. That is, the
moving sum becomes same to discrete-window based sum at the time that
aligns to the time window. This means using the pseudo moving sum based
nr_accesses makes no change to users who shows the value for every
aggregation interval.
Patches Sequence
----------------
The sequence of the patches is as follows. The first four patches are for
preparation of the change. The first two (patches 1 and 2) implements a
helper function for nr_accesses update and eliminate corner case that
skips use of the function, respectively. Following two (patches 3 and 4)
respectively implement the pseudo-moving sum function and its simple unit
test case.
Two patches for making DAMON to use the pseudo-moving sum follow. The
fifthe one (patch 5) introduces a new field for representing the
pseudo-moving sum-based access rate of each region, and the sixth one
makes the new representation to actually updated with the pseudo-moving
sum function.
Last two patches (patches 7 and 8) makes followup fixes for skipping
unnecessary updates and marking the moving sum function as static,
respectively.
This patch (of 8):
Each DAMON operarions set is updating nr_accesses field of each
damon_region for each of their access check results, from the
check_accesses() callback. Directly accessing the field could make things
complex to manage and change in future. Define and use a dedicated
function for the purpose.
Link: https://lkml.kernel.org/r/20230915025251.72816-1-sj@kernel.org
Link: https://lkml.kernel.org/r/20230915025251.72816-2-sj@kernel.org
Signed-off-by: SeongJae Park <sj@kernel.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>
2023-09-15 02:52:44 +00:00
|
|
|
if (accessed)
|
|
|
|
r->nr_accesses++;
|
|
|
|
}
|
|
|
|
|
2022-09-12 14:39:03 +00:00
|
|
|
static int __init damon_init(void)
|
|
|
|
{
|
|
|
|
damon_region_cache = KMEM_CACHE(damon_region, 0);
|
|
|
|
if (unlikely(!damon_region_cache)) {
|
|
|
|
pr_err("creating damon_region_cache fails\n");
|
|
|
|
return -ENOMEM;
|
|
|
|
}
|
|
|
|
|
|
|
|
return 0;
|
|
|
|
}
|
|
|
|
|
|
|
|
subsys_initcall(damon_init);
|
|
|
|
|
2021-09-08 02:57:09 +00:00
|
|
|
#include "core-test.h"
|