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With the advent of various new memory types, some machines will have multiple types of memory, e.g. DRAM and PMEM (persistent memory). The memory subsystem of these machines can be called memory tiering system, because the performance of the different types of memory are usually different. In such system, because of the memory accessing pattern changing etc, some pages in the slow memory may become hot globally. So in this patch, the NUMA balancing mechanism is enhanced to optimize the page placement among the different memory types according to hot/cold dynamically. In a typical memory tiering system, there are CPUs, fast memory and slow memory in each physical NUMA node. The CPUs and the fast memory will be put in one logical node (called fast memory node), while the slow memory will be put in another (faked) logical node (called slow memory node). That is, the fast memory is regarded as local while the slow memory is regarded as remote. So it's possible for the recently accessed pages in the slow memory node to be promoted to the fast memory node via the existing NUMA balancing mechanism. The original NUMA balancing mechanism will stop to migrate pages if the free memory of the target node becomes below the high watermark. This is a reasonable policy if there's only one memory type. But this makes the original NUMA balancing mechanism almost do not work to optimize page placement among different memory types. Details are as follows. It's the common cases that the working-set size of the workload is larger than the size of the fast memory nodes. Otherwise, it's unnecessary to use the slow memory at all. So, there are almost always no enough free pages in the fast memory nodes, so that the globally hot pages in the slow memory node cannot be promoted to the fast memory node. To solve the issue, we have 2 choices as follows, a. Ignore the free pages watermark checking when promoting hot pages from the slow memory node to the fast memory node. This will create some memory pressure in the fast memory node, thus trigger the memory reclaiming. So that, the cold pages in the fast memory node will be demoted to the slow memory node. b. Define a new watermark called wmark_promo which is higher than wmark_high, and have kswapd reclaiming pages until free pages reach such watermark. The scenario is as follows: when we want to promote hot-pages from a slow memory to a fast memory, but fast memory's free pages would go lower than high watermark with such promotion, we wake up kswapd with wmark_promo watermark in order to demote cold pages and free us up some space. So, next time we want to promote hot-pages we might have a chance of doing so. The choice "a" may create high memory pressure in the fast memory node. If the memory pressure of the workload is high, the memory pressure may become so high that the memory allocation latency of the workload is influenced, e.g. the direct reclaiming may be triggered. The choice "b" works much better at this aspect. If the memory pressure of the workload is high, the hot pages promotion will stop earlier because its allocation watermark is higher than that of the normal memory allocation. So in this patch, choice "b" is implemented. A new zone watermark (WMARK_PROMO) is added. Which is larger than the high watermark and can be controlled via watermark_scale_factor. In addition to the original page placement optimization among sockets, the NUMA balancing mechanism is extended to be used to optimize page placement according to hot/cold among different memory types. So the sysctl user space interface (numa_balancing) is extended in a backward compatible way as follow, so that the users can enable/disable these functionality individually. The sysctl is converted from a Boolean value to a bits field. The definition of the flags is, - 0: NUMA_BALANCING_DISABLED - 1: NUMA_BALANCING_NORMAL - 2: NUMA_BALANCING_MEMORY_TIERING We have tested the patch with the pmbench memory accessing benchmark with the 80:20 read/write ratio and the Gauss access address distribution on a 2 socket Intel server with Optane DC Persistent Memory Model. The test results shows that the pmbench score can improve up to 95.9%. Thanks Andrew Morton to help fix the document format error. Link: https://lkml.kernel.org/r/20220221084529.1052339-3-ying.huang@intel.com Signed-off-by: "Huang, Ying" <ying.huang@intel.com> Tested-by: Baolin Wang <baolin.wang@linux.alibaba.com> Reviewed-by: Baolin Wang <baolin.wang@linux.alibaba.com> Acked-by: Johannes Weiner <hannes@cmpxchg.org> Reviewed-by: Oscar Salvador <osalvador@suse.de> Reviewed-by: Yang Shi <shy828301@gmail.com> Cc: Michal Hocko <mhocko@suse.com> Cc: Rik van Riel <riel@surriel.com> Cc: Mel Gorman <mgorman@techsingularity.net> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Dave Hansen <dave.hansen@linux.intel.com> Cc: Zi Yan <ziy@nvidia.com> Cc: Wei Xu <weixugc@google.com> Cc: Shakeel Butt <shakeelb@google.com> Cc: zhongjiang-ali <zhongjiang-ali@linux.alibaba.com> Cc: Randy Dunlap <rdunlap@infradead.org> Cc: Feng Tang <feng.tang@intel.com> Signed-off-by: Andrew Morton <akpm@linux-foundation.org> Signed-off-by: Linus Torvalds <torvalds@linux-foundation.org> |
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