[PATCH v6 0/9] Multigenerational LRU Framework
Barry Song
21cnbao at gmail.com
Sat Jan 22 21:43:06 PST 2022
On Wed, Jan 5, 2022 at 7:17 PM Yu Zhao <yuzhao at google.com> wrote:
>
> 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.
>
> Design objectives
> =================
> The design objectives are:
> 1. Better representation of access recency
> 2. Try to profit from spatial locality
> 3. Clear fast path making obvious choices
> 4. Simple self-correcting heuristics
>
> The representation of access recency is at the core of all LRU
> approximations. The multigenerational LRU (MGLRU) divides pages into
> multiple lists (generations), each having bounded access recency (a
> time interval). Generations establish a common frame of reference and
> help make better choices, e.g., between different memcgs on a computer
> or different computers in a data center (for cluster job scheduling).
>
> Exploiting spatial locality improves the efficiency when gathering the
> accessed bit. A rmap walk targets a single page and doesn't try to
> profit from discovering an accessed PTE. A page table walk can sweep
> all hotspots in an address space, but its search space can be too
> large to make a profit. The key is to optimize both methods and use
> them in combination. (PMU is another option for further exploration.)
>
> Fast path reduces code complexity and runtime overhead. Unmapped pages
> don't require TLB flushes; clean pages don't require writeback. These
> facts are only helpful when other conditions, e.g., access recency,
> are similar. With generations as a common frame of reference,
> additional factors stand out. But obvious choices might not be good
> choices; thus self-correction is required (the next objective).
>
> The benefits of simple self-correcting heuristics are self-evident.
> Again with generations as a common frame of reference, this becomes
> attainable. Specifically, pages in the same generation are categorized
> based on additional factors, and a closed-loop control statistically
> compares the refault percentages across all categories and throttles
> the eviction of those that have higher percentages.
>
> Patchset overview
> =================
> 1. mm: x86, arm64: add arch_has_hw_pte_young()
> 2. mm: x86: add CONFIG_ARCH_HAS_NONLEAF_PMD_YOUNG
> Materializing hardware optimizations when trying to clear the accessed
> bit in many PTEs. If hardware automatically sets the accessed bit in
> PTEs, there is no need to worry about bursty page faults (emulating
> the accessed bit). If it also sets the accessed bit in non-leaf PMD
> entries, there is no need to search the PTE table pointed to by a PMD
> entry that doesn't have the accessed bit set.
>
> 3. mm/vmscan.c: refactor shrink_node()
> A minor refactor.
>
> 4. mm: multigenerational lru: groundwork
> Adding the basic data structure and the functions to initialize it and
> insert/remove pages.
>
> 5. mm: multigenerational lru: mm_struct list
> An infra keeps track of mm_struct's for page table walkers and
> provides them with optimizations, i.e., switch_mm() tracking and Bloom
> filters.
>
> 6. mm: multigenerational lru: aging
> 7. mm: multigenerational lru: eviction
> "The page reclaim" is a producer/consumer model. "The aging" produces
> cold pages, whereas "the eviction " consumes them. Cold pages flow
> through generations. The aging uses the mm_struct list infra to sweep
> dense hotspots in page tables. During a page table walk, the aging
> clears the accessed bit and tags accessed pages with the youngest
> generation number. The eviction sorts those pages when it encounters
> them. For pages in the oldest generation, eviction walks the rmap to
> check the accessed bit one more time before evicting them. During an
> rmap walk, the eviction feeds dense hotspots back to the aging. Dense
> hotspots flow through the Bloom filters. For pages not mapped in page
> tables, the eviction uses the PID controller to statistically
> determine whether they have higher refaults. If so, the eviction
> throttles their eviction by moving them to the next generation (the
> second oldest).
>
> 8. mm: multigenerational lru: user interface
> The knobs to turn on/off MGLRU and provide the userspace with
> thrashing prevention, working set estimation (the aging) and proactive
> reclaim (the eviction).
>
> 9. mm: multigenerational lru: Kconfig
> The Kconfig options.
>
> 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]. (These synthetic benchmarks
> are popular among MM developers, but we prefer large-scale A/B
> experiments to validate improvements.)
> 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/linux-mm/20211102002002.92051-1-bot@edi.works/
> [03] https://lore.kernel.org/linux-mm/20211009054315.47073-1-bot@edi.works/
> [04] https://lore.kernel.org/linux-mm/20211021194103.65648-1-bot@edi.works/
> [05] https://lore.kernel.org/linux-mm/20211109021346.50266-1-bot@edi.works/
> [06] https://lore.kernel.org/linux-mm/20211202062806.80365-1-bot@edi.works/
> [07] https://lore.kernel.org/linux-mm/20211209072416.33606-1-bot@edi.works/
> [08] https://lore.kernel.org/linux-mm/20211218071041.24077-1-bot@edi.works/
> [09] https://lore.kernel.org/linux-mm/20211122053248.57311-1-bot@edi.works/
> [10] https://lore.kernel.org/linux-mm/20220104202247.2903702-1-yuzhao@google.com/
>
> Read-world applications
> =======================
> Third-party testimonials
> ------------------------
> Konstantin wrote [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.
>
> The Arch Linux Zen kernel [12] has been using MGLRU since 5.12. Many
> of its users reported their positive experiences to me, e.g., Shivodit
> wrote:
> I've tried the latest Zen kernel (5.14.13-zen1-1-zen in the
> archlinux testing repos), everything's been smooth so far. I also
> decided to copy a large volume of files to check performance under
> I/O load, and everything went smoothly - no stuttering was present,
> everything was responsive.
>
> Large-scale deployments
> -----------------------
> We've rolled out MGLRU to tens of millions of Chrome OS users and
> about a million Android users. Google's fleetwide profiling [13] shows
> an overall 40% decrease in kswapd CPU usage, in addition to
Hi Yu,
Was the overall 40% decrease of kswap CPU usgae seen on x86 or arm64?
And I am curious how much we are taking advantage of NONLEAF_PMD_YOUNG.
Does it help a lot in decreasing the cpu usage? If so, this might be
a good proof that arm64 also needs this hardware feature?
In short, I am curious how much the improvement in this patchset depends
on the hardware ability of NONLEAF_PMD_YOUNG.
> 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
> rendering latency at the 50th percentile.
>
> [11] https://lore.kernel.org/linux-mm/140226722f2032c86301fbd326d91baefe3d7d23.camel@yandex.ru/
> [12] https://github.com/zen-kernel/zen-kernel/
> [13] https://research.google/pubs/pub44271/
>
Thanks
Barry
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