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8 commits
| Author | SHA1 | Message | Date | |
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Andrii Nakryiko
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bad2e478af |
selftests/bpf: Turn on libbpf 1.0 mode and fix all IS_ERR checks
Turn ony libbpf 1.0 mode. Fix all the explicit IS_ERR checks that now will be broken because libbpf returns NULL on error (and sets errno). Fix ASSERT_OK_PTR and ASSERT_ERR_PTR to work for both old mode and new modes and use them throughout selftests. This is trivial to do by using libbpf_get_error() API that all libbpf users are supposed to use, instead of IS_ERR checks. A bunch of checks also did explicit -1 comparison for various fd-returning APIs. Such checks are replaced with >= 0 or < 0 cases. There were also few misuses of bpf_object__find_map_by_name() in test_maps. Those are fixed in this patch as well. Signed-off-by: Andrii Nakryiko <andrii@kernel.org> Signed-off-by: Alexei Starovoitov <ast@kernel.org> Acked-by: John Fastabend <john.fastabend@gmail.com> Acked-by: Toke Høiland-Jørgensen <toke@redhat.com> Link: https://lore.kernel.org/bpf/20210525035935.1461796-3-andrii@kernel.org |
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Yang Li
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215cb7d382 |
bpf/benchs/bench_ringbufs: Remove unneeded semicolon
Eliminate the following coccicheck warning: ./tools/testing/selftests/bpf/benchs/bench_ringbufs.c:322:2-3: Unneeded semicolon Reported-by: Abaci Robot <abaci@linux.alibaba.com> Signed-off-by: Yang Li <yang.lee@linux.alibaba.com> Signed-off-by: Andrii Nakryiko <andrii@kernel.org> Link: https://lore.kernel.org/bpf/1612684360-115910-1-git-send-email-yang.lee@linux.alibaba.com |
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Toke Høiland-Jørgensen
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b000def2e0 |
selftests: Remove fmod_ret from test_overhead
The test_overhead prog_test included an fmod_ret program that attached to
__set_task_comm() in the kernel. However, this function was never listed as
allowed for return modification, so this only worked because of the
verifier skipping tests when a trampoline already existed for the attach
point. Now that the verifier checks have been fixed, remove fmod_ret from
the test so it works again.
Fixes:
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Alexei Starovoitov
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e68a144547 |
selftests/bpf: Add sleepable tests
Modify few tests to sanity test sleepable bpf functionality. Running 'bench trig-fentry-sleep' vs 'bench trig-fentry' and 'perf report': sleepable with SRCU: 3.86% bench [k] __srcu_read_unlock 3.22% bench [k] __srcu_read_lock 0.92% bench [k] bpf_prog_740d4210cdcd99a3_bench_trigger_fentry_sleep 0.50% bench [k] bpf_trampoline_10297 0.26% bench [k] __bpf_prog_exit_sleepable 0.21% bench [k] __bpf_prog_enter_sleepable sleepable with RCU_TRACE: 0.79% bench [k] bpf_prog_740d4210cdcd99a3_bench_trigger_fentry_sleep 0.72% bench [k] bpf_trampoline_10381 0.31% bench [k] __bpf_prog_exit_sleepable 0.29% bench [k] __bpf_prog_enter_sleepable non-sleepable with RCU: 0.88% bench [k] bpf_prog_740d4210cdcd99a3_bench_trigger_fentry 0.84% bench [k] bpf_trampoline_10297 0.13% bench [k] __bpf_prog_enter 0.12% bench [k] __bpf_prog_exit Signed-off-by: Alexei Starovoitov <ast@kernel.org> Signed-off-by: Daniel Borkmann <daniel@iogearbox.net> Acked-by: KP Singh <kpsingh@google.com> Link: https://lore.kernel.org/bpf/20200827220114.69225-6-alexei.starovoitov@gmail.com |
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Andrii Nakryiko
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c97099b0f2 |
bpf: Add BPF ringbuf and perf buffer benchmarks
Extend bench framework with ability to have benchmark-provided child argument
parser for custom benchmark-specific parameters. This makes bench generic code
modular and independent from any specific benchmark.
Also implement a set of benchmarks for new BPF ring buffer and existing perf
buffer. 4 benchmarks were implemented: 2 variations for each of BPF ringbuf
and perfbuf:,
- rb-libbpf utilizes stock libbpf ring_buffer manager for reading data;
- rb-custom implements custom ring buffer setup and reading code, to
eliminate overheads inherent in generic libbpf code due to callback
functions and the need to update consumer position after each consumed
record, instead of batching updates (due to pessimistic assumption that
user callback might take long time and thus could unnecessarily hold ring
buffer space for too long);
- pb-libbpf uses stock libbpf perf_buffer code with all the default
settings, though uses higher-performance raw event callback to minimize
unnecessary overhead;
- pb-custom implements its own custom consumer code to minimize any possible
overhead of generic libbpf implementation and indirect function calls.
All of the test support default, no data notification skipped, mode, as well
as sampled mode (with --rb-sampled flag), which allows to trigger epoll
notification less frequently and reduce overhead. As will be shown, this mode
is especially critical for perf buffer, which suffers from high overhead of
wakeups in kernel.
Otherwise, all benchamrks implement similar way to generate a batch of records
by using fentry/sys_getpgid BPF program, which pushes a bunch of records in
a tight loop and records number of successful and dropped samples. Each record
is a small 8-byte integer, to minimize the effect of memory copying with
bpf_perf_event_output() and bpf_ringbuf_output().
Benchmarks that have only one producer implement optional back-to-back mode,
in which record production and consumption is alternating on the same CPU.
This is the highest-throughput happy case, showing ultimate performance
achievable with either BPF ringbuf or perfbuf.
All the below scenarios are implemented in a script in
benchs/run_bench_ringbufs.sh. Tests were performed on 28-core/56-thread
Intel Xeon CPU E5-2680 v4 @ 2.40GHz CPU.
Single-producer, parallel producer
==================================
rb-libbpf 12.054 ± 0.320M/s (drops 0.000 ± 0.000M/s)
rb-custom 8.158 ± 0.118M/s (drops 0.001 ± 0.003M/s)
pb-libbpf 0.931 ± 0.007M/s (drops 0.000 ± 0.000M/s)
pb-custom 0.965 ± 0.003M/s (drops 0.000 ± 0.000M/s)
Single-producer, parallel producer, sampled notification
========================================================
rb-libbpf 11.563 ± 0.067M/s (drops 0.000 ± 0.000M/s)
rb-custom 15.895 ± 0.076M/s (drops 0.000 ± 0.000M/s)
pb-libbpf 9.889 ± 0.032M/s (drops 0.000 ± 0.000M/s)
pb-custom 9.866 ± 0.028M/s (drops 0.000 ± 0.000M/s)
Single producer on one CPU, consumer on another one, both running at full
speed. Curiously, rb-libbpf has higher throughput than objectively faster (due
to more lightweight consumer code path) rb-custom. It appears that faster
consumer causes kernel to send notifications more frequently, because consumer
appears to be caught up more frequently. Performance of perfbuf suffers from
default "no sampling" policy and huge overhead that causes.
In sampled mode, rb-custom is winning very significantly eliminating too
frequent in-kernel wakeups, the gain appears to be more than 2x.
Perf buffer achieves even more impressive wins, compared to stock perfbuf
settings, with 10x improvements in throughput with 1:500 sampling rate. The
trade-off is that with sampling, application might not get next X events until
X+1st arrives, which is not always acceptable. With steady influx of events,
though, this shouldn't be a problem.
Overall, single-producer performance of ring buffers seems to be better no
matter the sampled/non-sampled modes, but it especially beats ring buffer
without sampling due to its adaptive notification approach.
Single-producer, back-to-back mode
==================================
rb-libbpf 15.507 ± 0.247M/s (drops 0.000 ± 0.000M/s)
rb-libbpf-sampled 14.692 ± 0.195M/s (drops 0.000 ± 0.000M/s)
rb-custom 21.449 ± 0.157M/s (drops 0.000 ± 0.000M/s)
rb-custom-sampled 20.024 ± 0.386M/s (drops 0.000 ± 0.000M/s)
pb-libbpf 1.601 ± 0.015M/s (drops 0.000 ± 0.000M/s)
pb-libbpf-sampled 8.545 ± 0.064M/s (drops 0.000 ± 0.000M/s)
pb-custom 1.607 ± 0.022M/s (drops 0.000 ± 0.000M/s)
pb-custom-sampled 8.988 ± 0.144M/s (drops 0.000 ± 0.000M/s)
Here we test a back-to-back mode, which is arguably best-case scenario both
for BPF ringbuf and perfbuf, because there is no contention and for ringbuf
also no excessive notification, because consumer appears to be behind after
the first record. For ringbuf, custom consumer code clearly wins with 21.5 vs
16 million records per second exchanged between producer and consumer. Sampled
mode actually hurts a bit due to slightly slower producer logic (it needs to
fetch amount of data available to decide whether to skip or force notification).
Perfbuf with wakeup sampling gets 5.5x throughput increase, compared to
no-sampling version. There also doesn't seem to be noticeable overhead from
generic libbpf handling code.
Perfbuf back-to-back, effect of sample rate
===========================================
pb-sampled-1 1.035 ± 0.012M/s (drops 0.000 ± 0.000M/s)
pb-sampled-5 3.476 ± 0.087M/s (drops 0.000 ± 0.000M/s)
pb-sampled-10 5.094 ± 0.136M/s (drops 0.000 ± 0.000M/s)
pb-sampled-25 7.118 ± 0.153M/s (drops 0.000 ± 0.000M/s)
pb-sampled-50 8.169 ± 0.156M/s (drops 0.000 ± 0.000M/s)
pb-sampled-100 8.887 ± 0.136M/s (drops 0.000 ± 0.000M/s)
pb-sampled-250 9.180 ± 0.209M/s (drops 0.000 ± 0.000M/s)
pb-sampled-500 9.353 ± 0.281M/s (drops 0.000 ± 0.000M/s)
pb-sampled-1000 9.411 ± 0.217M/s (drops 0.000 ± 0.000M/s)
pb-sampled-2000 9.464 ± 0.167M/s (drops 0.000 ± 0.000M/s)
pb-sampled-3000 9.575 ± 0.273M/s (drops 0.000 ± 0.000M/s)
This benchmark shows the effect of event sampling for perfbuf. Back-to-back
mode for highest throughput. Just doing every 5th record notification gives
3.5x speed up. 250-500 appears to be the point of diminishing return, with
almost 9x speed up. Most benchmarks use 500 as the default sampling for pb-raw
and pb-custom.
Ringbuf back-to-back, effect of sample rate
===========================================
rb-sampled-1 1.106 ± 0.010M/s (drops 0.000 ± 0.000M/s)
rb-sampled-5 4.746 ± 0.149M/s (drops 0.000 ± 0.000M/s)
rb-sampled-10 7.706 ± 0.164M/s (drops 0.000 ± 0.000M/s)
rb-sampled-25 12.893 ± 0.273M/s (drops 0.000 ± 0.000M/s)
rb-sampled-50 15.961 ± 0.361M/s (drops 0.000 ± 0.000M/s)
rb-sampled-100 18.203 ± 0.445M/s (drops 0.000 ± 0.000M/s)
rb-sampled-250 19.962 ± 0.786M/s (drops 0.000 ± 0.000M/s)
rb-sampled-500 20.881 ± 0.551M/s (drops 0.000 ± 0.000M/s)
rb-sampled-1000 21.317 ± 0.532M/s (drops 0.000 ± 0.000M/s)
rb-sampled-2000 21.331 ± 0.535M/s (drops 0.000 ± 0.000M/s)
rb-sampled-3000 21.688 ± 0.392M/s (drops 0.000 ± 0.000M/s)
Similar benchmark for ring buffer also shows a great advantage (in terms of
throughput) of skipping notifications. Skipping every 5th one gives 4x boost.
Also similar to perfbuf case, 250-500 seems to be the point of diminishing
returns, giving roughly 20x better results.
Keep in mind, for this test, notifications are controlled manually with
BPF_RB_NO_WAKEUP and BPF_RB_FORCE_WAKEUP. As can be seen from previous
benchmarks, adaptive notifications based on consumer's positions provides same
(or even slightly better due to simpler load generator on BPF side) benefits in
favorable back-to-back scenario. Over zealous and fast consumer, which is
almost always caught up, will make thoughput numbers smaller. That's the case
when manual notification control might prove to be extremely beneficial.
Ringbuf back-to-back, reserve+commit vs output
==============================================
reserve 22.819 ± 0.503M/s (drops 0.000 ± 0.000M/s)
output 18.906 ± 0.433M/s (drops 0.000 ± 0.000M/s)
Ringbuf sampled, reserve+commit vs output
=========================================
reserve-sampled 15.350 ± 0.132M/s (drops 0.000 ± 0.000M/s)
output-sampled 14.195 ± 0.144M/s (drops 0.000 ± 0.000M/s)
BPF ringbuf supports two sets of APIs with various usability and performance
tradeoffs: bpf_ringbuf_reserve()+bpf_ringbuf_commit() vs bpf_ringbuf_output().
This benchmark clearly shows superiority of reserve+commit approach, despite
using a small 8-byte record size.
Single-producer, consumer/producer competing on the same CPU, low batch count
=============================================================================
rb-libbpf 3.045 ± 0.020M/s (drops 3.536 ± 0.148M/s)
rb-custom 3.055 ± 0.022M/s (drops 3.893 ± 0.066M/s)
pb-libbpf 1.393 ± 0.024M/s (drops 0.000 ± 0.000M/s)
pb-custom 1.407 ± 0.016M/s (drops 0.000 ± 0.000M/s)
This benchmark shows one of the worst-case scenarios, in which producer and
consumer do not coordinate *and* fight for the same CPU. No batch count and
sampling settings were able to eliminate drops for ringbuffer, producer is
just too fast for consumer to keep up. But ringbuf and perfbuf still able to
pass through quite a lot of messages, which is more than enough for a lot of
applications.
Ringbuf, multi-producer contention
==================================
rb-libbpf nr_prod 1 10.916 ± 0.399M/s (drops 0.000 ± 0.000M/s)
rb-libbpf nr_prod 2 4.931 ± 0.030M/s (drops 0.000 ± 0.000M/s)
rb-libbpf nr_prod 3 4.880 ± 0.006M/s (drops 0.000 ± 0.000M/s)
rb-libbpf nr_prod 4 3.926 ± 0.004M/s (drops 0.000 ± 0.000M/s)
rb-libbpf nr_prod 8 4.011 ± 0.004M/s (drops 0.000 ± 0.000M/s)
rb-libbpf nr_prod 12 3.967 ± 0.016M/s (drops 0.000 ± 0.000M/s)
rb-libbpf nr_prod 16 2.604 ± 0.030M/s (drops 0.001 ± 0.002M/s)
rb-libbpf nr_prod 20 2.233 ± 0.003M/s (drops 0.000 ± 0.000M/s)
rb-libbpf nr_prod 24 2.085 ± 0.015M/s (drops 0.000 ± 0.000M/s)
rb-libbpf nr_prod 28 2.055 ± 0.004M/s (drops 0.000 ± 0.000M/s)
rb-libbpf nr_prod 32 1.962 ± 0.004M/s (drops 0.000 ± 0.000M/s)
rb-libbpf nr_prod 36 2.089 ± 0.005M/s (drops 0.000 ± 0.000M/s)
rb-libbpf nr_prod 40 2.118 ± 0.006M/s (drops 0.000 ± 0.000M/s)
rb-libbpf nr_prod 44 2.105 ± 0.004M/s (drops 0.000 ± 0.000M/s)
rb-libbpf nr_prod 48 2.120 ± 0.058M/s (drops 0.000 ± 0.001M/s)
rb-libbpf nr_prod 52 2.074 ± 0.024M/s (drops 0.007 ± 0.014M/s)
Ringbuf uses a very short-duration spinlock during reservation phase, to check
few invariants, increment producer count and set record header. This is the
biggest point of contention for ringbuf implementation. This benchmark
evaluates the effect of multiple competing writers on overall throughput of
a single shared ringbuffer.
Overall throughput drops almost 2x when going from single to two
highly-contended producers, gradually dropping with additional competing
producers. Performance drop stabilizes at around 20 producers and hovers
around 2mln even with 50+ fighting producers, which is a 5x drop compared to
non-contended case. Good kernel implementation in kernel helps maintain decent
performance here.
Note, that in the intended real-world scenarios, it's not expected to get even
close to such a high levels of contention. But if contention will become
a problem, there is always an option of sharding few ring buffers across a set
of CPUs.
Signed-off-by: Andrii Nakryiko <andriin@fb.com>
Signed-off-by: Daniel Borkmann <daniel@iogearbox.net>
Link: https://lore.kernel.org/bpf/20200529075424.3139988-5-andriin@fb.com
Signed-off-by: Alexei Starovoitov <ast@kernel.org>
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Andrii Nakryiko
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c5d420c32c |
selftest/bpf: Add BPF triggering benchmark
It is sometimes desirable to be able to trigger BPF program from user-space with minimal overhead. sys_enter would seem to be a good candidate, yet in a lot of cases there will be a lot of noise from syscalls triggered by other processes on the system. So while searching for low-overhead alternative, I've stumbled upon getpgid() syscall, which seems to be specific enough to not suffer from accidental syscall by other apps. This set of benchmarks compares tp, raw_tp w/ filtering by syscall ID, kprobe, fentry and fmod_ret with returning error (so that syscall would not be executed), to determine the lowest-overhead way. Here are results on my machine (using benchs/run_bench_trigger.sh script): base : 9.200 ± 0.319M/s tp : 6.690 ± 0.125M/s rawtp : 8.571 ± 0.214M/s kprobe : 6.431 ± 0.048M/s fentry : 8.955 ± 0.241M/s fmodret : 8.903 ± 0.135M/s So it seems like fmodret doesn't give much benefit for such lightweight syscall. Raw tracepoint is pretty decent despite additional filtering logic, but it will be called for any other syscall in the system, which rules it out. Fentry, though, seems to be adding the least amoung of overhead and achieves 97.3% of performance of baseline no-BPF-attached syscall. Using getpgid() seems to be preferable to set_task_comm() approach from test_overhead, as it's about 2.35x faster in a baseline performance. Signed-off-by: Andrii Nakryiko <andriin@fb.com> Signed-off-by: Alexei Starovoitov <ast@kernel.org> Acked-by: John Fastabend <john.fastabend@gmail.com> Acked-by: Yonghong Song <yhs@fb.com> Link: https://lore.kernel.org/bpf/20200512192445.2351848-5-andriin@fb.com |
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Andrii Nakryiko
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4eaf0b5c5e |
selftest/bpf: Fmod_ret prog and implement test_overhead as part of bench
Add fmod_ret BPF program to existing test_overhead selftest. Also re-implement user-space benchmarking part into benchmark runner to compare results. Results with ./bench are consistently somewhat lower than test_overhead's, but relative performance of various types of BPF programs stay consisten (e.g., kretprobe is noticeably slower). This slowdown seems to be coming from the fact that test_overhead is single-threaded, while benchmark always spins off at least one thread for producer. This has been confirmed by hacking multi-threaded test_overhead variant and also single-threaded bench variant. Resutls are below. run_bench_rename.sh script from benchs/ subdirectory was used to produce results for ./bench. Single-threaded implementations =============================== /* bench: single-threaded, atomics */ base : 4.622 ± 0.049M/s kprobe : 3.673 ± 0.052M/s kretprobe : 2.625 ± 0.052M/s rawtp : 4.369 ± 0.089M/s fentry : 4.201 ± 0.558M/s fexit : 4.309 ± 0.148M/s fmodret : 4.314 ± 0.203M/s /* selftest: single-threaded, no atomics */ task_rename base 4555K events per sec task_rename kprobe 3643K events per sec task_rename kretprobe 2506K events per sec task_rename raw_tp 4303K events per sec task_rename fentry 4307K events per sec task_rename fexit 4010K events per sec task_rename fmod_ret 3984K events per sec Multi-threaded implementations ============================== /* bench: multi-threaded w/ atomics */ base : 3.910 ± 0.023M/s kprobe : 3.048 ± 0.037M/s kretprobe : 2.300 ± 0.015M/s rawtp : 3.687 ± 0.034M/s fentry : 3.740 ± 0.087M/s fexit : 3.510 ± 0.009M/s fmodret : 3.485 ± 0.050M/s /* selftest: multi-threaded w/ atomics */ task_rename base 3872K events per sec task_rename kprobe 3068K events per sec task_rename kretprobe 2350K events per sec task_rename raw_tp 3731K events per sec task_rename fentry 3639K events per sec task_rename fexit 3558K events per sec task_rename fmod_ret 3511K events per sec /* selftest: multi-threaded, no atomics */ task_rename base 3945K events per sec task_rename kprobe 3298K events per sec task_rename kretprobe 2451K events per sec task_rename raw_tp 3718K events per sec task_rename fentry 3782K events per sec task_rename fexit 3543K events per sec task_rename fmod_ret 3526K events per sec Note that the fact that ./bench benchmark always uses atomic increments for counting, while test_overhead doesn't, doesn't influence test results all that much. Signed-off-by: Andrii Nakryiko <andriin@fb.com> Signed-off-by: Alexei Starovoitov <ast@kernel.org> Acked-by: John Fastabend <john.fastabend@gmail.com> Acked-by: Yonghong Song <yhs@fb.com> Link: https://lore.kernel.org/bpf/20200512192445.2351848-4-andriin@fb.com |
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Andrii Nakryiko
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8e7c2a023a |
selftests/bpf: Add benchmark runner infrastructure
While working on BPF ringbuf implementation, testing, and benchmarking, I've developed a pretty generic and modular benchmark runner, which seems to be generically useful, as I've already used it for one more purpose (testing fastest way to trigger BPF program, to minimize overhead of in-kernel code). This patch adds generic part of benchmark runner and sets up Makefile for extending it with more sets of benchmarks. Benchmarker itself operates by spinning up specified number of producer and consumer threads, setting up interval timer sending SIGALARM signal to application once a second. Every second, current snapshot with hits/drops counters are collected and stored in an array. Drops are useful for producer/consumer benchmarks in which producer might overwhelm consumers. Once test finishes after given amount of warm-up and testing seconds, mean and stddev are calculated (ignoring warm-up results) and is printed out to stdout. This setup seems to give consistent and accurate results. To validate behavior, I added two atomic counting tests: global and local. For global one, all the producer threads are atomically incrementing same counter as fast as possible. This, of course, leads to huge drop of performance once there is more than one producer thread due to CPUs fighting for the same memory location. Local counting, on the other hand, maintains one counter per each producer thread, incremented independently. Once per second, all counters are read and added together to form final "counting throughput" measurement. As expected, such setup demonstrates linear scalability with number of producers (as long as there are enough physical CPU cores, of course). See example output below. Also, this setup can nicely demonstrate disastrous effects of false sharing, if care is not taken to take those per-producer counters apart into independent cache lines. Demo output shows global counter first with 1 producer, then with 4. Both total and per-producer performance significantly drop. The last run is local counter with 4 producers, demonstrating near-perfect scalability. $ ./bench -a -w1 -d2 -p1 count-global Setting up benchmark 'count-global'... Benchmark 'count-global' started. Iter 0 ( 24.822us): hits 148.179M/s (148.179M/prod), drops 0.000M/s Iter 1 ( 37.939us): hits 149.308M/s (149.308M/prod), drops 0.000M/s Iter 2 (-10.774us): hits 150.717M/s (150.717M/prod), drops 0.000M/s Iter 3 ( 3.807us): hits 151.435M/s (151.435M/prod), drops 0.000M/s Summary: hits 150.488 ± 1.079M/s (150.488M/prod), drops 0.000 ± 0.000M/s $ ./bench -a -w1 -d2 -p4 count-global Setting up benchmark 'count-global'... Benchmark 'count-global' started. Iter 0 ( 60.659us): hits 53.910M/s ( 13.477M/prod), drops 0.000M/s Iter 1 (-17.658us): hits 53.722M/s ( 13.431M/prod), drops 0.000M/s Iter 2 ( 5.865us): hits 53.495M/s ( 13.374M/prod), drops 0.000M/s Iter 3 ( 0.104us): hits 53.606M/s ( 13.402M/prod), drops 0.000M/s Summary: hits 53.608 ± 0.113M/s ( 13.402M/prod), drops 0.000 ± 0.000M/s $ ./bench -a -w1 -d2 -p4 count-local Setting up benchmark 'count-local'... Benchmark 'count-local' started. Iter 0 ( 23.388us): hits 640.450M/s (160.113M/prod), drops 0.000M/s Iter 1 ( 2.291us): hits 605.661M/s (151.415M/prod), drops 0.000M/s Iter 2 ( -6.415us): hits 607.092M/s (151.773M/prod), drops 0.000M/s Iter 3 ( -1.361us): hits 601.796M/s (150.449M/prod), drops 0.000M/s Summary: hits 604.849 ± 2.739M/s (151.212M/prod), drops 0.000 ± 0.000M/s Benchmark runner supports setting thread affinity for producer and consumer threads. You can use -a flag for default CPU selection scheme, where first consumer gets CPU #0, next one gets CPU #1, and so on. Then producer threads pick up next CPU and increment one-by-one as well. But user can also specify a set of CPUs independently for producers and consumers with --prod-affinity 1,2-10,15 and --cons-affinity <set-of-cpus>. The latter allows to force producers and consumers to share same set of CPUs, if necessary. Signed-off-by: Andrii Nakryiko <andriin@fb.com> Signed-off-by: Alexei Starovoitov <ast@kernel.org> Acked-by: Yonghong Song <yhs@fb.com> Link: https://lore.kernel.org/bpf/20200512192445.2351848-3-andriin@fb.com |