mm: x86, arm64: add arch_has_hw_pte_young()
Patch series "Multi-Gen LRU Framework", v14. What's new ========== 1. OpenWrt, in addition to Android, Arch Linux Zen, Armbian, ChromeOS, Liquorix, post-factum and XanMod, is now shipping MGLRU on 5.15. 2. Fixed long-tailed direct reclaim latency seen on high-memory (TBs) machines. The old direct reclaim backoff, which tries to enforce a minimum fairness among all eligible memcgs, over-swapped by about (total_mem>>DEF_PRIORITY)-nr_to_reclaim. The new backoff, which pulls the plug on swapping once the target is met, trades some fairness for curtailed latency: https://lore.kernel.org/r/20220918080010.2920238-10-yuzhao@google.com/ 3. Fixed minior build warnings and conflicts. More comments and nits. TLDR ==== The current page reclaim is too expensive in terms of CPU usage and it often makes poor choices about what to evict. This patchset offers an alternative solution that is performant, versatile and straightforward. Patchset overview ================= The design and implementation overview is in patch 14: https://lore.kernel.org/r/20220918080010.2920238-15-yuzhao@google.com/ 01. mm: x86, arm64: add arch_has_hw_pte_young() 02. mm: x86: add CONFIG_ARCH_HAS_NONLEAF_PMD_YOUNG Take advantage of hardware features when trying to clear the accessed bit in many PTEs. 03. mm/vmscan.c: refactor shrink_node() 04. Revert "include/linux/mm_inline.h: fold __update_lru_size() into its sole caller" Minor refactors to improve readability for the following patches. 05. mm: multi-gen LRU: groundwork Adds the basic data structure and the functions that insert pages to and remove pages from the multi-gen LRU (MGLRU) lists. 06. mm: multi-gen LRU: minimal implementation A minimal implementation without optimizations. 07. mm: multi-gen LRU: exploit locality in rmap Exploits spatial locality to improve efficiency when using the rmap. 08. mm: multi-gen LRU: support page table walks Further exploits spatial locality by optionally scanning page tables. 09. mm: multi-gen LRU: optimize multiple memcgs Optimizes the overall performance for multiple memcgs running mixed types of workloads. 10. mm: multi-gen LRU: kill switch Adds a kill switch to enable or disable MGLRU at runtime. 11. mm: multi-gen LRU: thrashing prevention 12. mm: multi-gen LRU: debugfs interface Provide userspace with features like thrashing prevention, working set estimation and proactive reclaim. 13. mm: multi-gen LRU: admin guide 14. mm: multi-gen LRU: design doc Add an admin guide and a design doc. Benchmark results ================= Independent lab results ----------------------- Based on the popularity of searches [01] and the memory usage in Google's public cloud, the most popular open-source memory-hungry applications, in alphabetical order, are: Apache Cassandra Memcached Apache Hadoop MongoDB Apache Spark PostgreSQL MariaDB (MySQL) Redis An independent lab evaluated MGLRU with the most widely used benchmark suites for the above applications. They posted 960 data points along with kernel metrics and perf profiles collected over more than 500 hours of total benchmark time. Their final reports show that, with 95% confidence intervals (CIs), the above applications all performed significantly better for at least part of their benchmark matrices. On 5.14: 1. Apache Spark [02] took 95% CIs [9.28, 11.19]% and [12.20, 14.93]% less wall time to sort three billion random integers, respectively, under the medium- and the high-concurrency conditions, when overcommitting memory. There were no statistically significant changes in wall time for the rest of the benchmark matrix. 2. MariaDB [03] achieved 95% CIs [5.24, 10.71]% and [20.22, 25.97]% more transactions per minute (TPM), respectively, under the medium- and the high-concurrency conditions, when overcommitting memory. There were no statistically significant changes in TPM for the rest of the benchmark matrix. 3. Memcached [04] achieved 95% CIs [23.54, 32.25]%, [20.76, 41.61]% and [21.59, 30.02]% more operations per second (OPS), respectively, for sequential access, random access and Gaussian (distribution) access, when THP=always; 95% CIs [13.85, 15.97]% and [23.94, 29.92]% more OPS, respectively, for random access and Gaussian access, when THP=never. There were no statistically significant changes in OPS for the rest of the benchmark matrix. 4. MongoDB [05] achieved 95% CIs [2.23, 3.44]%, [6.97, 9.73]% and [2.16, 3.55]% more operations per second (OPS), respectively, for exponential (distribution) access, random access and Zipfian (distribution) access, when underutilizing memory; 95% CIs [8.83, 10.03]%, [21.12, 23.14]% and [5.53, 6.46]% more OPS, respectively, for exponential access, random access and Zipfian access, when overcommitting memory. On 5.15: 5. Apache Cassandra [06] achieved 95% CIs [1.06, 4.10]%, [1.94, 5.43]% and [4.11, 7.50]% more operations per second (OPS), respectively, for exponential (distribution) access, random access and Zipfian (distribution) access, when swap was off; 95% CIs [0.50, 2.60]%, [6.51, 8.77]% and [3.29, 6.75]% more OPS, respectively, for exponential access, random access and Zipfian access, when swap was on. 6. Apache Hadoop [07] took 95% CIs [5.31, 9.69]% and [2.02, 7.86]% less average wall time to finish twelve parallel TeraSort jobs, respectively, under the medium- and the high-concurrency conditions, when swap was on. There were no statistically significant changes in average wall time for the rest of the benchmark matrix. 7. PostgreSQL [08] achieved 95% CI [1.75, 6.42]% more transactions per minute (TPM) under the high-concurrency condition, when swap was off; 95% CIs [12.82, 18.69]% and [22.70, 46.86]% more TPM, respectively, under the medium- and the high-concurrency conditions, when swap was on. There were no statistically significant changes in TPM for the rest of the benchmark matrix. 8. Redis [09] achieved 95% CIs [0.58, 5.94]%, [6.55, 14.58]% and [11.47, 19.36]% more total operations per second (OPS), respectively, for sequential access, random access and Gaussian (distribution) access, when THP=always; 95% CIs [1.27, 3.54]%, [10.11, 14.81]% and [8.75, 13.64]% more total OPS, respectively, for sequential access, random access and Gaussian access, when THP=never. Our lab results --------------- To supplement the above results, we ran the following benchmark suites on 5.16-rc7 and found no regressions [10]. fs_fio_bench_hdd_mq pft fs_lmbench pgsql-hammerdb fs_parallelio redis fs_postmark stream hackbench sysbenchthread kernbench tpcc_spark memcached unixbench multichase vm-scalability mutilate will-it-scale nginx [01] https://trends.google.com [02] https://lore.kernel.org/r/20211102002002.92051-1-bot@edi.works/ [03] https://lore.kernel.org/r/20211009054315.47073-1-bot@edi.works/ [04] https://lore.kernel.org/r/20211021194103.65648-1-bot@edi.works/ [05] https://lore.kernel.org/r/20211109021346.50266-1-bot@edi.works/ [06] https://lore.kernel.org/r/20211202062806.80365-1-bot@edi.works/ [07] https://lore.kernel.org/r/20211209072416.33606-1-bot@edi.works/ [08] https://lore.kernel.org/r/20211218071041.24077-1-bot@edi.works/ [09] https://lore.kernel.org/r/20211122053248.57311-1-bot@edi.works/ [10] https://lore.kernel.org/r/20220104202247.2903702-1-yuzhao@google.com/ Read-world applications ======================= Third-party testimonials ------------------------ Konstantin reported [11]: I have Archlinux with 8G RAM + zswap + swap. While developing, I have lots of apps opened such as multiple LSP-servers for different langs, chats, two browsers, etc... Usually, my system gets quickly to a point of SWAP-storms, where I have to kill LSP-servers, restart browsers to free memory, etc, otherwise the system lags heavily and is barely usable. 1.5 day ago I migrated from 5.11.15 kernel to 5.12 + the LRU patchset, and I started up by opening lots of apps to create memory pressure, and worked for a day like this. Till now I had not a single SWAP-storm, and mind you I got 3.4G in SWAP. I was never getting to the point of 3G in SWAP before without a single SWAP-storm. Vaibhav from IBM reported [12]: In a synthetic MongoDB Benchmark, seeing an average of ~19% throughput improvement on POWER10(Radix MMU + 64K Page Size) with MGLRU patches on top of 5.16 kernel for MongoDB + YCSB across three different request distributions, namely, Exponential, Uniform and Zipfan. Shuang from U of Rochester reported [13]: With the MGLRU, fio achieved 95% CIs [38.95, 40.26]%, [4.12, 6.64]% and [9.26, 10.36]% higher throughput, respectively, for random access, Zipfian (distribution) access and Gaussian (distribution) access, when the average number of jobs per CPU is 1; 95% CIs [42.32, 49.15]%, [9.44, 9.89]% and [20.99, 22.86]% higher throughput, respectively, for random access, Zipfian access and Gaussian access, when the average number of jobs per CPU is 2. Daniel from Michigan Tech reported [14]: With Memcached allocating ~100GB of byte-addressable Optante, performance improvement in terms of throughput (measured as queries per second) was about 10% for a series of workloads. Large-scale deployments ----------------------- We've rolled out MGLRU to tens of millions of ChromeOS users and about a million Android users. Google's fleetwide profiling [15] shows an overall 40% decrease in kswapd CPU usage, in addition to improvements in other UX metrics, e.g., an 85% decrease in the number of low-memory kills at the 75th percentile and an 18% decrease in app launch time at the 50th percentile. The downstream kernels that have been using MGLRU include: 1. Android [16] 2. Arch Linux Zen [17] 3. Armbian [18] 4. ChromeOS [19] 5. Liquorix [20] 6. OpenWrt [21] 7. post-factum [22] 8. XanMod [23] [11] https://lore.kernel.org/r/140226722f2032c86301fbd326d91baefe3d7d23.camel@yandex.ru/ [12] https://lore.kernel.org/r/87czj3mux0.fsf@vajain21.in.ibm.com/ [13] https://lore.kernel.org/r/20220105024423.26409-1-szhai2@cs.rochester.edu/ [14] https://lore.kernel.org/r/CA+4-3vksGvKd18FgRinxhqHetBS1hQekJE2gwco8Ja-bJWKtFw@mail.gmail.com/ [15] https://dl.acm.org/doi/10.1145/2749469.2750392 [16] https://android.com [17] https://archlinux.org [18] https://armbian.com [19] https://chromium.org [20] https://liquorix.net [21] https://openwrt.org [22] https://codeberg.org/pf-kernel [23] https://xanmod.org Summary ======= The facts are: 1. The independent lab results and the real-world applications indicate substantial improvements; there are no known regressions. 2. Thrashing prevention, working set estimation and proactive reclaim work out of the box; there are no equivalent solutions. 3. There is a lot of new code; no smaller changes have been demonstrated similar effects. Our options, accordingly, are: 1. Given the amount of evidence, the reported improvements will likely materialize for a wide range of workloads. 2. Gauging the interest from the past discussions, the new features will likely be put to use for both personal computers and data centers. 3. Based on Google's track record, the new code will likely be well maintained in the long term. It'd be more difficult if not impossible to achieve similar effects with other approaches. This patch (of 14): Some architectures automatically set the accessed bit in PTEs, e.g., x86 and arm64 v8.2. On architectures that do not have this capability, clearing the accessed bit in a PTE usually triggers a page fault following the TLB miss of this PTE (to emulate the accessed bit). Being aware of this capability can help make better decisions, e.g., whether to spread the work out over a period of time to reduce bursty page faults when trying to clear the accessed bit in many PTEs. Note that theoretically this capability can be unreliable, e.g., hotplugged CPUs might be different from builtin ones. Therefore it should not be used in architecture-independent code that involves correctness, e.g., to determine whether TLB flushes are required (in combination with the accessed bit). Link: https://lkml.kernel.org/r/20220918080010.2920238-1-yuzhao@google.com Link: https://lkml.kernel.org/r/20220918080010.2920238-2-yuzhao@google.comSigned-off-by:Yu Zhao <yuzhao@google.com> Reviewed-by:
Barry Song <baohua@kernel.org> Acked-by:
Brian Geffon <bgeffon@google.com> Acked-by:
Jan Alexander Steffens (heftig) <heftig@archlinux.org> Acked-by:
Oleksandr Natalenko <oleksandr@natalenko.name> Acked-by:
Steven Barrett <steven@liquorix.net> Acked-by:
Suleiman Souhlal <suleiman@google.com> Acked-by:
Will Deacon <will@kernel.org> Tested-by:
Daniel Byrne <djbyrne@mtu.edu> Tested-by:
Donald Carr <d@chaos-reins.com> Tested-by:
Holger Hoffstätte <holger@applied-asynchrony.com> Tested-by:
Konstantin Kharlamov <Hi-Angel@yandex.ru> Tested-by:
Shuang Zhai <szhai2@cs.rochester.edu> Tested-by:
Sofia Trinh <sofia.trinh@edi.works> Tested-by:
Vaibhav Jain <vaibhav@linux.ibm.com> Cc: Andi Kleen <ak@linux.intel.com> Cc: Aneesh Kumar K.V <aneesh.kumar@linux.ibm.com> Cc: Catalin Marinas <catalin.marinas@arm.com> Cc: Dave Hansen <dave.hansen@linux.intel.com> Cc: Hillf Danton <hdanton@sina.com> Cc: Jens Axboe <axboe@kernel.dk> Cc: Johannes Weiner <hannes@cmpxchg.org> Cc: Jonathan Corbet <corbet@lwn.net> Cc: Linus Torvalds <torvalds@linux-foundation.org> Cc: linux-arm-kernel@lists.infradead.org Cc: Matthew Wilcox <willy@infradead.org> Cc: Mel Gorman <mgorman@suse.de> Cc: Michael Larabel <Michael@MichaelLarabel.com> Cc: Michal Hocko <mhocko@kernel.org> Cc: Mike Rapoport <rppt@kernel.org> Cc: Peter Zijlstra <peterz@infradead.org> Cc: Tejun Heo <tj@kernel.org> Cc: Vlastimil Babka <vbabka@suse.cz> Cc: Miaohe Lin <linmiaohe@huawei.com> Cc: Mike Rapoport <rppt@linux.ibm.com> Cc: Qi Zheng <zhengqi.arch@bytedance.com> Signed-off-by:
Andrew Morton <akpm@linux-foundation.org>
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