From: SeongJae Park <email@example.com> To: <firstname.lastname@example.org> Cc: SeongJae Park <email@example.com>, <Jonathan.Cameron@Huawei.com>, <firstname.lastname@example.org>, <email@example.com>, <firstname.lastname@example.org>, <email@example.com>, <firstname.lastname@example.org>, <email@example.com>, <firstname.lastname@example.org>, <email@example.com>, <firstname.lastname@example.org>, <email@example.com>, <firstname.lastname@example.org>, <email@example.com>, <firstname.lastname@example.org>, <email@example.com>, <firstname.lastname@example.org>, <email@example.com>, <firstname.lastname@example.org>, <email@example.com>, <firstname.lastname@example.org>, <email@example.com>, <firstname.lastname@example.org>, <email@example.com>, <firstname.lastname@example.org>, <email@example.com>, <firstname.lastname@example.org>, <email@example.com>, <firstname.lastname@example.org>, <email@example.com>, <firstname.lastname@example.org>, <email@example.com>, <firstname.lastname@example.org>, <email@example.com>, <firstname.lastname@example.org>, <email@example.com>, <firstname.lastname@example.org>, <email@example.com>, <firstname.lastname@example.org> Subject: [PATCH v10 00/16] Introduce Data Access MONitor (DAMON) Date: Tue, 5 May 2020 13:07:59 +0200 Message-ID: <email@example.com> (raw) From: SeongJae Park <firstname.lastname@example.org> Introduction ============ Memory management decisions can be improved if finer data access information is available. However, because such finer information usually comes with higher overhead, most systems including Linux forgives the potential benefit and rely on only coarse information or some light-weight heuristics. The pseudo-LRU and the aggressive THP promotions are such examples. A number of data access pattern awared memory management optimizations (refer to 'Appendix A' for more details) consistently say the potential benefit is not small. However, none of those has successfully merged to the mainline Linux kernel mainly due to the absence of a scalable and efficient data access monitoring mechanism. Refer to 'Appendix B' to see the limitations of existing memory monitoring mechanisms. DAMON is a data access monitoring subsystem for the problem. It is 1) accurate enough to be used for the DRAM level memory management (a straightforward DAMON-based optimization achieved up to 2.55x speedup), 2) light-weight enough to be applied online (compared to a straightforward access monitoring scheme, DAMON is up to 94,242.42x lighter) and 3) keeps predefined upper-bound overhead regardless of the size of target workloads (thus scalable). Refer to 'Appendix C' if you interested in how it is possible, and 'Appendix F' to know how the numbers collected. DAMON has mainly designed for the kernel's memory management mechanisms. However, because it is implemented as a standalone kernel module and provides several interfaces, it can be used by a wide range of users including kernel space programs, user space programs, programmers, and administrators. DAMON is now supporting the monitoring only, but it will also provide simple and convenient data access pattern awared memory managements by itself. Refer to 'Appendix D' for more detailed expected usages of DAMON. Boring? Here Are Something Colorful =================================== For intuitive understanding of DAMON, I made web pages[1-8] showing the visualized dynamic data access pattern of various realistic workloads in PARSEC3 and SPLASH-2X bechmark suites. The figures are generated using the user space tool in 10th patch of this patchset. There are pages showing the heatmap format dynamic access pattern of each workload for heap area, mmap()-ed area, and stack area. I splitted the entire address space to the three area because there are huge unmapped regions between the areas. You can also show how the dynamic working set size of each workload is distributed, and how it is chronologically changing. The most important characteristic of DAMON is its promise of the upperbound of the monitoring overhead. To show whether DAMON keeps the promise well, I visualized the number of monitoring operations required for each 5 milliseconds, which is configured to not exceed 1000. You can show the distribution of the numbers and how it changes chronologically.  https://damonitor.github.io/reports/latest/by_image/heatmap.0.png.html  https://damonitor.github.io/reports/latest/by_image/heatmap.1.png.html  https://damonitor.github.io/reports/latest/by_image/heatmap.2.png.html  https://damonitor.github.io/reports/latest/by_image/wss_sz.png.html  https://damonitor.github.io/reports/latest/by_image/wss_time.png.html  https://damonitor.github.io/reports/latest/by_image/nr_regions_sz.png.html  https://damonitor.github.io/reports/latest/by_image/nr_regions_time.png.html Future Plans ============ This patchset is only for the first stage of DAMON. As soon as this patchset is merged, official patchsets for below future plans will be posted. Automate Data Access Pattern-aware Memory Management ---------------------------------------------------- Though DAMON provides the monitoring feature, implementing data access pattern aware memory management schemes could be difficult to beginners. DAMON will be able to do most of the work by itself in near future. Users will be required to only describe what kind of data access monitoring-based operation schemes they want. By applying a very simple scheme for THP promotion/demotion with a latest version of the patchset (not posted yet), DAMON achieved 18x lower memory space overhead compared to THP while preserving about 50% of the THP performance benefit with SPLASH-2X benchmark suite. An RFC patchset for this plan is already available (https://email@example.com/). Support Various Address Spaces ------------------------------ Currently, DAMON supports virtual memory address spaces using PTE Accessed bits as its access checking primitive. However, the core design of DAMON is not dependent to such implementation details. In a future, DAMON will decouple those and support various address spaces including physical memory. It will further allow users to configure and even implement the primitives by themselves for their special usecase. Monitoring of page cache, NUMA nodes, specific files, or block devices would be examples of such usecases. An RFC patchset for this plan is already available (https://firstname.lastname@example.org/). Frequently Asked Questions ========================== Q: Why a new module, instead of extending perf or other tools? A: First, DAMON aims to be used by other programs including the kernel. Therefore, having dependency to specific tools like perf is not desirable. Second, because it need to be lightweight as much as possible so that it can be used online, any unnecessary overhead such as kernel - user space context switching cost should be avoided. These are the two most biggest reasons why DAMON is implemented in the kernel space. The idle page tracking subsystem would be the kernel module that most seems similar to DAMON. However, its own interface is not compatible with DAMON. Also, the internal implementation of it has no common part to be reused by DAMON. Q: Can 'perf mem' or PMUs used instead of DAMON? A: No. Roughly speaking, DAMON has two seperate layers. The low layer is access check of pages, and the higher layer is its core mechanisms for overhead controlling. For the low layer, DAMON is now using the PTE Accessed bits. Other H/W or S/W features that can be used for the access check of pages, such as 'perf mem', PMU, or even page idle, could be used instead in the layer. However, those could not alternate the high layer of DAMON. Evaluations =========== We evaluated DAMON's overhead, monitoring quality and usefulness using 25 realistic workloads on my QEMU/KVM based virtual machine. DAMON is lightweight. It consumes only -0.03% more system memory and up to 1% CPU time. It makes target worloads only 1.02% slower. DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP removes 88.43% of THP memory overheads while preserving 36.36% of THP speedup. Another experimental DAMON-based 'proactive reclamation' implementation reduces 86.37% of residentail sets and 22% of system memory footprint while incurring only 2.03% runtime overhead in best case (parsec3/freqmine). NOTE that the experimentail THP optimization and proactive reclamation are not for production, just only for proof of concepts. Please refer to 'Appendix E' for detailed evaluation setup and results. References ========== Prototypes of DAMON have introduced by an LPC kernel summit track talk and two academic papers[2,3]. Please refer to those for more detailed information, especially the evaluations. The latest version of the patchsets has also introduced by an LWN artice.  SeongJae Park, Tracing Data Access Pattern with Bounded Overhead and Best-effort Accuracy. In The Linux Kernel Summit, September 2019. https://linuxplumbersconf.org/event/4/contributions/548/  SeongJae Park, Yunjae Lee, Heon Y. Yeom, Profiling Dynamic Data Access Patterns with Controlled Overhead and Quality. In 20th ACM/IFIP International Middleware Conference Industry, December 2019. https://dl.acm.org/doi/10.1145/3366626.3368125  SeongJae Park, Yunjae Lee, Yunhee Kim, Heon Y. Yeom, Profiling Dynamic Data Access Patterns with Bounded Overhead and Accuracy. In IEEE International Workshop on Foundations and Applications of Self- Systems (FAS 2019), June 2019.  Jonathan Corbet, Memory-management optimization with DAMON. In Linux Weekly News (LWN), Feb 2020. https://lwn.net/Articles/812707/ Baseline and Complete Git Trees =============================== The patches are based on the v5.6. You can also clone the complete git tree: $ git clone git://github.com/sjp38/linux -b damon/patches/v10 The web is also available: https://github.com/sjp38/linux/releases/tag/damon/patches/v10 This patchset contains patches for the stabled main logic of DAMON only. The latest DAMON development tree is also available at: https://github.com/sjp38/linux/tree/damon/master Sequence Of Patches =================== The patches are organized in the following sequence. The first two patches are preparation of DAMON patchset. The 1st patch adds typos found in previous versions of DAMON patchset to 'scripts/spelling.txt' so that the typos can be caught by 'checkpatch.pl'. The 2nd patch exports 'lookup_page_ext()' to GPL modules so that it can be used by DAMON even though it is built as a loadable module. Next five patches implement the core of DAMON and it's programming interface. The 3rd patch introduces DAMON module, it's data structures, and data structure related common functions. Following four patches (4nd to 7th) implements the core mechanisms of DAMON, namely regions based sampling (patch 4), adaptive regions adjustment (patches 5-6), and dynamic memory mapping chage adoption (patch 7). Following four patches are for low level users of DAMON. The 8th patch implements callbacks for each of monitoring steps so that users can do whatever they want with the access patterns. The 9th one implements recording of access patterns in DAMON for better convenience and efficiency. Each of next two patches (10th and 11th) respectively adds a debugfs interface for privileged people and/or programs in user space, and a tracepoint for other tracepoints supporting tracers such as perf. Two patches for high level users of DAMON follows. To provide a minimal reference to the debugfs interface and for high level use/tests of the DAMON, the next patch (12th) implements an user space tool. The 13th patch adds a document for administrators of DAMON. Next two patches are for tests. The 14th and 15th patches provide unit tests (based on kunit) and user space tests (based on kselftest), respectively. Finally, the last patch (16th) updates the MAINTAINERS file. Patch History ============= The most biggest change in this version is support of minimal region size, which defaults to 'PAGE_SIZE'. This change will reduce unnecessary region splits and thus improve the quality of the output. In a future, we will be able to make this configurable for support of various access check primitives such as PMUs. Changes from v9 (https://email@example.com/) - Split each region into 4 subregions if possible (Jonathan Cameraon) - Update kunit test for the split code change Changes from v8 (https://firstname.lastname@example.org/) - Make regions always aligned by minimal region size that can be changed (Stefan Nuernberger) - Store binary format version in the recording file (Stefan Nuernberger) - Use 'int' for pid instead of 'unsigned long' (Stefan Nuernberger) - Fix a race condition in damon thread termination (Stefan Nuernberger) - Optimize random value generation and recording (Stefan Nuernberger) - Clean up commit messages and comments (Stefan Nuernberger) - Clean up code (Stefan Nuernberger) - Use explicit signalling and 'do_exit()' for damon thread termination - Add more typos to spelling.txt - Update the performance evaluation results - Describe future plans in the cover letter Changes from v7 (https://email@example.com/) - Cleanup variable names (Jonathan Cameron) - Split sampling address setup from access_check() (Jonathan Cameron) - Make sampling address to always locate in the region (Jonathan Cameron) - Make initial region's sampling addr to be old (Jonathan Cameron) - Split kdamond on/off function to seperate functions (Jonathan Cameron) - Fix wrong kernel doc comments (Jonathan Cameron) - Reset 'last_accessed' to false in kdamond_check_access() if necessary - Rebase on v5.6 Changes from v6 (https://firstname.lastname@example.org/) - Wordsmith cover letter (Shakeel Butt) - Cleanup code and commit messages (Jonathan Cameron) - Avoid kthread_run() under spinlock critical section (Jonathan Cameron) - Use kthread_stop() (Jonathan Cameron) - Change tracepoint to trace regions (Jonathan Cameron) - Implement API from the beginning (Jonathan Cameron) - Fix typos (Jonathan Cameron) - Fix access checking to properly handle regions smaller than single page (Jonathan Cameron) - Add found typos to 'scripts/spelling.txt' - Add recent evaluation results including DAMON-based Operation Schemes Changes from v5 (https://email@example.com/) - Fix minor bugs (sampling, record attributes, debugfs and user space tool) - selftests: Add debugfs interface tests for the bugs - Modify the user space tool to use its self default values for parameters - Fix pmg huge page access check Changes from v4 (https://firstname.lastname@example.org/) - Add 'Reviewed-by' for the kunit tests patch (Brendan Higgins) - Make the unit test to depedns on 'DAMON=y' (Randy Dunlap and kbuild bot) Reported-by: kbuild test robot <email@example.com> - Fix m68k module build issue Reported-by: kbuild test robot <firstname.lastname@example.org> - Add selftests - Seperate patches for low level users from core logics for better reading - Clean up debugfs interface - Trivial nitpicks Changes from v3 (https://email@example.com/) - Fix i386 build issue Reported-by: kbuild test robot <firstname.lastname@example.org> - Increase the default size of the monitoring result buffer to 1 MiB - Fix misc bugs in debugfs interface Changes from v2 (https://email@example.com/) - Move MAINTAINERS changes to last commit (Brendan Higgins) - Add descriptions for kunittest: why not only entire mappings and what the 4 input sets are trying to test (Brendan Higgins) - Remove 'kdamond_need_stop()' test (Brendan Higgins) - Discuss about the 'perf mem' and DAMON (Peter Zijlstra) - Make CV clearly say what it actually does (Peter Zijlstra) - Answer why new module (Qian Cai) - Diable DAMON by default (Randy Dunlap) - Change the interface: Seperate recording attributes (attrs, record, rules) and allow multiple kdamond instances - Implement kernel API interface Changes from v1 (https://firstname.lastname@example.org/) - Rebase on v5.5 - Add a tracepoint for integration with other tracers (Kirill A. Shutemov) - document: Add more description for the user space tool (Brendan Higgins) - unittest: Improve readability (Brendan Higgins) - unittest: Use consistent name and helpers function (Brendan Higgins) - Update PG_Young to avoid reclaim logic interference (Yunjae Lee) Changes from RFC (https://email@example.com/) - Specify an ambiguous plan of access pattern based mm optimizations - Support loadable module build - Cleanup code SeongJae Park (16): scripts/spelling: Add a few more typos mm/page_ext: Export lookup_page_ext() to GPL modules mm: Introduce Data Access MONitor (DAMON) mm/damon: Implement region based sampling mm/damon: Adaptively adjust regions mm/damon: Split regions into 4 subregions if possible mm/damon: Apply dynamic memory mapping changes mm/damon: Implement callbacks mm/damon: Implement access pattern recording mm/damon: Add debugfs interface mm/damon: Add tracepoints tools: Add a minimal user-space tool for DAMON Documentation/admin-guide/mm: Add a document for DAMON mm/damon: Add kunit tests mm/damon: Add user space selftests MAINTAINERS: Update for DAMON .../admin-guide/mm/data_access_monitor.rst | 428 +++++ Documentation/admin-guide/mm/index.rst | 1 + MAINTAINERS | 12 + include/linux/damon.h | 78 + include/trace/events/damon.h | 43 + mm/Kconfig | 23 + mm/Makefile | 1 + mm/damon-test.h | 622 +++++++ mm/damon.c | 1504 +++++++++++++++++ mm/page_ext.c | 1 + scripts/spelling.txt | 8 + tools/damon/.gitignore | 1 + tools/damon/_dist.py | 36 + tools/damon/_recfile.py | 23 + tools/damon/bin2txt.py | 67 + tools/damon/damo | 37 + tools/damon/heats.py | 362 ++++ tools/damon/nr_regions.py | 91 + tools/damon/record.py | 212 +++ tools/damon/report.py | 45 + tools/damon/wss.py | 97 ++ tools/testing/selftests/damon/Makefile | 7 + .../selftests/damon/_chk_dependency.sh | 28 + tools/testing/selftests/damon/_chk_record.py | 108 ++ .../testing/selftests/damon/debugfs_attrs.sh | 139 ++ .../testing/selftests/damon/debugfs_record.sh | 50 + 26 files changed, 4024 insertions(+) create mode 100644 Documentation/admin-guide/mm/data_access_monitor.rst create mode 100644 include/linux/damon.h create mode 100644 include/trace/events/damon.h create mode 100644 mm/damon-test.h create mode 100644 mm/damon.c create mode 100644 tools/damon/.gitignore create mode 100644 tools/damon/_dist.py create mode 100644 tools/damon/_recfile.py create mode 100644 tools/damon/bin2txt.py create mode 100755 tools/damon/damo create mode 100644 tools/damon/heats.py create mode 100644 tools/damon/nr_regions.py create mode 100644 tools/damon/record.py create mode 100644 tools/damon/report.py create mode 100644 tools/damon/wss.py create mode 100644 tools/testing/selftests/damon/Makefile create mode 100644 tools/testing/selftests/damon/_chk_dependency.sh create mode 100644 tools/testing/selftests/damon/_chk_record.py create mode 100755 tools/testing/selftests/damon/debugfs_attrs.sh create mode 100755 tools/testing/selftests/damon/debugfs_record.sh -- 2.17.1 ================================== >8 ========================================= Appendix A: Related Works ========================= There are a number of researches[1,2,3,4,5,6] optimizing memory management mechanisms based on the actual memory access patterns that shows impressive results. However, most of those has no deep consideration about the monitoring of the accesses itself. Some of those focused on the overhead of the monitoring, but does not consider the accuracy scalability or has additional dependencies. Indeed, one recent research about the proactive reclamation has also proposed to the kernel community but the monitoring overhead was considered a main problem.  Subramanya R Dulloor, Amitabha Roy, Zheguang Zhao, Narayanan Sundaram, Nadathur Satish, Rajesh Sankaran, Jeff Jackson, and Karsten Schwan. 2016. Data tiering in heterogeneous memory systems. In Proceedings of the 11th European Conference on Computer Systems (EuroSys). ACM, 15.  Youngjin Kwon, Hangchen Yu, Simon Peter, Christopher J Rossbach, and Emmett Witchel. 2016. Coordinated and efficient huge page management with ingens. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI). 705–721.  Harald Servat, Antonio J Peña, Germán Llort, Estanislao Mercadal, HansChristian Hoppe, and Jesús Labarta. 2017. Automating the application data placement in hybrid memory systems. In 2017 IEEE International Conference on Cluster Computing (CLUSTER). IEEE, 126–136.  Vlad Nitu, Boris Teabe, Alain Tchana, Canturk Isci, and Daniel Hagimont. 2018. Welcome to zombieland: practical and energy-efficient memory disaggregation in a datacenter. In Proceedings of the 13th European Conference on Computer Systems (EuroSys). ACM, 16.  Andres Lagar-Cavilla, Junwhan Ahn, Suleiman Souhlal, Neha Agarwal, Radoslaw Burny, Shakeel Butt, Jichuan Chang, Ashwin Chaugule, Nan Deng, Junaid Shahid, Greg Thelen, Kamil Adam Yurtsever, Yu Zhao, and Parthasarathy Ranganathan. 2019. Software-Defined Far Memory in Warehouse-Scale Computers. In Proceedings of the 24th International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). ACM, New York, NY, USA, 317–330. DOI:https://doi.org/10.1145/3297858.3304053  Carl Waldspurger, Trausti Saemundsson, Irfan Ahmad, and Nohhyun Park. 2017. Cache Modeling and Optimization using Miniature Simulations. In 2017 USENIX Annual Technical Conference (ATC). USENIX Association, Santa Clara, CA, 487–498. https://www.usenix.org/conference/atc17/technical-sessions/  Haojie Wang, Jidong Zhai, Xiongchao Tang, Bowen Yu, Xiaosong Ma, and Wenguang Chen. 2018. Spindle: Informed Memory Access Monitoring. In 2018 USENIX Annual Technical Conference (ATC). USENIX Association, Boston, MA, 561–574. https://www.usenix.org/conference/atc18/presentation/wang-haojie  Jonathan Corbet. 2019. Proactively reclaiming idle memory. (2019). https://lwn.net/Articles/787611/. Appendix B: Limitations of Other Access Monitoring Techniques ============================================================= The memory access instrumentation techniques which are applied to many tools such as Intel PIN is essential for correctness required cases such as memory access bug detections or cache level optimizations. However, those usually incur exceptionally high overhead which is unacceptable. Periodic access checks based on access counting features (e.g., PTE Accessed bits or PG_Idle flags) can reduce the overhead. It sacrifies some of the quality but it's still ok to many of this domain. However, the overhead arbitrarily increase as the size of the target workload grows. Miniature-like static region based sampling can set the upperbound of the overhead, but it will now decrease the quality of the output as the size of the workload grows. DAMON is another solution that overcomes the limitations. It is 1) accurate enough for this domain, 2) light-weight so that it can be applied online, and 3) allow users to set the upper-bound of the overhead, regardless of the size of target workloads. It is implemented as a simple and small kernel module to support various users in both of the user space and the kernel space. Refer to 'Evaluations' section below for detailed performance of DAMON. For the goals, DAMON utilizes its two core mechanisms, which allows lightweight overhead and high quality of output, repectively. To show how DAMON promises those, refer to 'Mechanisms of DAMON' section below. Appendix C: Mechanisms of DAMON =============================== Basic Access Check ------------------ DAMON basically reports what pages are how frequently accessed. The report is passed to users in binary format via a ``result file`` which users can set it's path. Note that the frequency is not an absolute number of accesses, but a relative frequency among the pages of the target workloads. Users can also control the resolution of the reports by setting two time intervals, ``sampling interval`` and ``aggregation interval``. In detail, DAMON checks access to each page per ``sampling interval``, aggregates the results (counts the number of the accesses to each page), and reports the aggregated results per ``aggregation interval``. For the access check of each page, DAMON uses the Accessed bits of PTEs. This is thus similar to the previously mentioned periodic access checks based mechanisms, which overhead is increasing as the size of the target process grows. Region Based Sampling --------------------- To avoid the unbounded increase of the overhead, DAMON groups a number of adjacent pages that assumed to have same access frequencies into a region. As long as the assumption (pages in a region have same access frequencies) is kept, only one page in the region is required to be checked. Thus, for each ``sampling interval``, DAMON randomly picks one page in each region and clears its Accessed bit. After one more ``sampling interval``, DAMON reads the Accessed bit of the page and increases the access frequency of the region if the bit has set meanwhile. Therefore, the monitoring overhead is controllable by setting the number of regions. DAMON allows users to set the minimal and maximum number of regions for the trade-off. Except the assumption, this is almost same with the above-mentioned miniature-like static region based sampling. In other words, this scheme cannot preserve the quality of the output if the assumption is not guaranteed. Adaptive Regions Adjustment --------------------------- At the beginning of the monitoring, DAMON constructs the initial regions by evenly splitting the memory mapped address space of the process into the user-specified minimal number of regions. In this initial state, the assumption is normally not kept and thus the quality could be low. To keep the assumption as much as possible, DAMON adaptively merges and splits each region. For each ``aggregation interval``, it compares the access frequencies of adjacent regions and merges those if the frequency difference is small. Then, after it reports and clears the aggregated access frequency of each region, it splits each region into two regions if the total number of regions is smaller than the half of the user-specified maximum number of regions. In this way, DAMON provides its best-effort quality and minimal overhead while keeping the bounds users set for their trade-off. Applying Dynamic Memory Mappings -------------------------------- Only a number of small parts in the super-huge virtual address space of the processes is mapped to physical memory and accessed. Thus, tracking the unmapped address regions is just wasteful. However, tracking every memory mapping change might incur an overhead. For the reason, DAMON applies the dynamic memory mapping changes to the tracking regions only for each of an user-specified time interval (``regions update interval``). Appendix D: Expected Use-cases ============================== A straightforward usecase of DAMON would be the program behavior analysis. With the DAMON output, users can confirm whether the program is running as intended or not. This will be useful for debuggings and tests of design points. The monitored results can also be useful for counting the dynamic working set size of workloads. For the administration of memory overcommitted systems or selection of the environments (e.g., containers providing different amount of memory) for your workloads, this will be useful. If you are a programmer, you can optimize your program by managing the memory based on the actual data access pattern. For example, you can identify the dynamic hotness of your data using DAMON and call ``mlock()`` to keep your hot data in DRAM, or call ``madvise()`` with ``MADV_PAGEOUT`` to proactively reclaim cold data. Even though your program is guaranteed to not encounter memory pressure, you can still improve the performance by applying the DAMON outputs for call of ``MADV_HUGEPAGE`` and ``MADV_NOHUGEPAGE``. More creative optimizations would be possible. Our evaluations of DAMON includes a straightforward optimization using the ``mlock()``. Please refer to the below Evaluation section for more detail. As DAMON incurs very low overhead, such optimizations can be applied not only offline, but also online. Also, there is no reason to limit such optimizations to the user space. Several parts of the kernel's memory management mechanisms could be also optimized using DAMON. The reclamation, the THP (de)promotion decisions, and the compaction would be such a candidates. DAMON will continue its development to be highly optimized for the online/in-kernel uses. We will further automate the optimization for many usecases. Appendix E: Evaluations ======================= Setup ----- On my personal QEMU/KVM based virtual machine on an Intel i7 host machine running Ubuntu 18.04, I measure runtime and consumed system memory while running various realistic workloads with several configurations. I use 13 and 12 workloads in PARSEC3 and SPLASH-2X benchmark suites, respectively. I personally use another wrapper scripts for setup and run of the workloads. On top of this patchset, we also applied the DAMON-based operation schemes patchset for this evaluation. Measurement ~~~~~~~~~~~ For the measurement of the amount of consumed memory in system global scope, I drop caches before starting each of the workloads and monitor 'MemFree' in the '/proc/meminfo' file. To make results more stable, I repeat the runs 5 times and average results. You can get stdev, min, and max of the numbers among the repeated runs in appendix below. Configurations ~~~~~~~~~~~~~~ The configurations I use are as below. orig: Linux v5.5 with 'madvise' THP policy rec: 'orig' plus DAMON running with record feature thp: same with 'orig', but use 'always' THP policy ethp: 'orig' plus a DAMON operation scheme, 'efficient THP' prcl: 'orig' plus a DAMON operation scheme, 'proactive reclaim' I use 'rec' for measurement of DAMON overheads to target workloads and system memory. The remaining configs including 'thp', 'ethp', and 'prcl' are for measurement of DAMON monitoring accuracy. 'ethp' and 'prcl' is simple DAMON-based operation schemes developed for proof of concepts of DAMON. 'ethp' reduces memory space waste of THP by using DAMON for decision of promotions and demotion for huge pages, while 'prcl' is as similar as the original work. Those are implemented as below: # format: <min/max size> <min/max frequency (0-100)> <min/max age> <action> # ethp: Use huge pages if a region >2MB shows >5% access rate, use regular # pages if a region >2MB shows <5% access rate for >1 second 2M null 5 null null null hugepage 2M null null 5 1s null nohugepage # prcl: If a region >4KB shows <5% access rate for >5 seconds, page out. 4K null null 5 500ms null pageout Note that both 'ethp' and 'prcl' are designed with my only straightforward intuition, because those are for only proof of concepts and monitoring accuracy of DAMON. In other words, those are not for production. For production use, those should be tuned more.  "Redis latency problems troubleshooting", https://redis.io/topics/latency  "Disable Transparent Huge Pages (THP)", https://docs.mongodb.com/manual/tutorial/transparent-huge-pages/  "The PARSEC Becnhmark Suite", https://parsec.cs.princeton.edu/index.htm  "SPLASH-2x", https://parsec.cs.princeton.edu/parsec3-doc.htm#splash2x  "parsec3_on_ubuntu", https://github.com/sjp38/parsec3_on_ubuntu  "[RFC v4 0/7] Implement Data Access Monitoring-based Memory Operation Schemes", https://firstname.lastname@example.org/  "Proactively reclaiming idle memory", https://lwn.net/Articles/787611/ Results ------- Below two tables show the measurement results. The runtimes are in seconds while the memory usages are in KiB. Each configurations except 'orig' shows its overhead relative to 'orig' in percent within parenthesises. runtime orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead) parsec3/blackscholes 106.654 107.111 (0.43) 106.622 (-0.03) 107.143 (0.46) 112.081 (5.09) parsec3/bodytrack 78.866 79.204 (0.43) 78.922 (0.07) 79.332 (0.59) 80.734 (2.37) parsec3/canneal 138.029 145.336 (5.29) 123.243 (-10.71) 132.810 (-3.78) 186.616 (35.20) parsec3/dedup 11.884 11.853 (-0.26) 11.659 (-1.89) 11.810 (-0.62) 12.782 (7.56) parsec3/facesim 207.510 208.426 (0.44) 204.354 (-1.52) 205.985 (-0.73) 216.421 (4.29) parsec3/ferret 190.833 191.105 (0.14) 190.544 (-0.15) 191.256 (0.22) 192.457 (0.85) parsec3/fluidanimate 212.443 211.528 (-0.43) 209.050 (-1.60) 211.874 (-0.27) 221.745 (4.38) parsec3/freqmine 290.628 291.023 (0.14) 289.766 (-0.30) 290.372 (-0.09) 296.525 (2.03) parsec3/raytrace 118.753 119.071 (0.27) 119.469 (0.60) 119.164 (0.35) 129.302 (8.88) parsec3/streamcluster 323.478 333.131 (2.98) 283.777 (-12.27) 292.393 (-9.61) 334.684 (3.46) parsec3/swaptions 153.842 155.696 (1.21) 154.910 (0.69) 155.416 (1.02) 154.564 (0.47) parsec3/vips 58.720 59.134 (0.71) 58.578 (-0.24) 58.704 (-0.03) 60.198 (2.52) parsec3/x264 67.249 69.709 (3.66) 72.050 (7.14) 66.221 (-1.53) 73.243 (8.91) splash2x/barnes 80.294 80.925 (0.79) 73.913 (-7.95) 79.315 (-1.22) 104.004 (29.53) splash2x/fft 33.382 33.455 (0.22) 22.975 (-31.18) 32.540 (-2.52) 41.816 (25.26) splash2x/lu_cb 85.227 86.203 (1.14) 84.441 (-0.92) 85.754 (0.62) 89.219 (4.68) splash2x/lu_ncb 92.584 93.778 (1.29) 90.515 (-2.23) 94.927 (2.53) 100.555 (8.61) splash2x/ocean_cp 44.616 44.908 (0.66) 43.252 (-3.06) 44.247 (-0.83) 47.246 (5.90) splash2x/ocean_ncp 81.833 81.842 (0.01) 51.142 (-37.50) 73.403 (-10.30) 135.436 (65.50) splash2x/radiosity 91.400 91.677 (0.30) 90.272 (-1.23) 91.934 (0.58) 101.606 (11.17) splash2x/radix 31.300 31.513 (0.68) 25.124 (-19.73) 31.029 (-0.87) 43.364 (38.54) splash2x/raytrace 84.216 84.812 (0.71) 82.784 (-1.70) 83.842 (-0.44) 85.805 (1.89) splash2x/volrend 87.021 88.504 (1.70) 86.420 (-0.69) 87.585 (0.65) 89.024 (2.30) splash2x/water_nsquared 232.134 233.488 (0.58) 221.293 (-4.67) 228.383 (-1.62) 236.080 (1.70) splash2x/water_spatial 89.560 89.473 (-0.10) 89.051 (-0.57) 90.450 (0.99) 98.599 (10.09) total 2992.450 3022.890 (1.02) 2864.120 (-4.29) 2945.880 (-1.56) 3244.110 (8.41) memused.avg orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead) parsec3/blackscholes 1821608.200 1835268.800 (0.75) 1832919.000 (0.62) 1833142.200 (0.63) 1628095.400 (-10.62) parsec3/bodytrack 1418964.600 1430433.200 (0.81) 1416871.200 (-0.15) 1433869.600 (1.05) 1427908.000 (0.63) parsec3/canneal 1045208.200 1058310.200 (1.25) 1037149.400 (-0.77) 1050233.400 (0.48) 1043619.200 (-0.15) parsec3/dedup 2436947.000 2457476.800 (0.84) 2439557.800 (0.11) 2433319.800 (-0.15) 2441437.000 (0.18) parsec3/facesim 540414.600 552894.400 (2.31) 540916.800 (0.09) 553387.000 (2.40) 485772.200 (-10.11) parsec3/ferret 319888.200 332860.800 (4.06) 317343.400 (-0.80) 330101.000 (3.19) 329613.200 (3.04) parsec3/fluidanimate 571528.400 585670.200 (2.47) 581304.600 (1.71) 588979.000 (3.05) 464640.400 (-18.70) parsec3/freqmine 985397.400 998531.400 (1.33) 994316.000 (0.91) 996987.000 (1.18) 768600.400 (-22.00) parsec3/raytrace 1744482.200 1754726.400 (0.59) 1743206.600 (-0.07) 1751374.600 (0.40) 1582949.400 (-9.26) parsec3/streamcluster 121025.400 140085.400 (15.75) 118762.200 (-1.87) 134685.400 (11.29) 132044.800 (9.11) parsec3/swaptions 13818.000 29163.000 (111.05) 13360.200 (-3.31) 28003.400 (102.66) 25973.000 (87.96) parsec3/vips 2958208.000 2979560.200 (0.72) 2954836.800 (-0.11) 2962623.400 (0.15) 2966417.000 (0.28) parsec3/x264 3183295.400 3203048.400 (0.62) 3196805.200 (0.42) 3191084.600 (0.24) 3215451.800 (1.01) splash2x/barnes 1214667.000 1213394.400 (-0.10) 1217076.200 (0.20) 1211348.400 (-0.27) 955056.000 (-21.37) splash2x/fft 9431970.200 9233116.800 (-2.11) 9281561.000 (-1.59) 9177215.600 (-2.70) 9459883.200 (0.30) splash2x/lu_cb 515966.400 525217.800 (1.79) 520740.200 (0.93) 521936.800 (1.16) 356931.000 (-30.82) splash2x/lu_ncb 514713.400 525678.000 (2.13) 517579.200 (0.56) 522634.800 (1.54) 368449.800 (-28.42) splash2x/ocean_cp 3339236.400 3282117.600 (-1.71) 3385554.400 (1.39) 3292656.800 (-1.39) 3274836.400 (-1.93) splash2x/ocean_ncp 3903327.200 3916898.800 (0.35) 7066337.800 (81.03) 4398342.000 (12.68) 3533107.600 (-9.48) splash2x/radiosity 1465314.800 1471012.200 (0.39) 1478023.600 (0.87) 1470864.400 (0.38) 567732.000 (-61.26) splash2x/radix 1715460.000 1675025.600 (-2.36) 1385497.000 (-19.23) 1654754.200 (-3.54) 2013401.600 (17.37) splash2x/raytrace 46682.400 59543.400 (27.55) 50391.400 (7.95) 61513.200 (31.77) 52070.800 (11.54) splash2x/volrend 152453.800 166313.000 (9.09) 152284.000 (-0.11) 164212.400 (7.71) 158362.800 (3.88) splash2x/water_nsquared 48045.400 61679.800 (28.38) 48871.200 (1.72) 59778.000 (24.42) 59444.200 (23.73) splash2x/water_spatial 667431.000 674130.400 (1.00) 665027.800 (-0.36) 674607.000 (1.08) 520460.800 (-22.02) total 40175831.000 40162000.000 (-0.03) 42956400.000 (6.92) 40497585.000 (0.80) 37832100.000 (-5.83) DAMON Overheads ~~~~~~~~~~~~~~~ In total, DAMON recording feature incurs 1.02% runtime overhead (up to 5.29% in worst case with 'parsec3/canneal') and -0.03% memory space overhead. For convenience test run of 'rec', I use a Python wrapper. The wrapper constantly consumes about 10-15MB of memory. This becomes high memory overhead if the target workload has small memory footprint. In detail, for parsec3/swaptions (13 MiB), splash2x/raytrace (46 MiB), splash2x/volrend (151 MiB), and splash2x/water_nsquared (48 MiB)), it shows 111%, 27%, 9%, and 28% overheads, respectively. Nonetheless, the overheads are not from DAMON, but from the wrapper, and thus should be ignored. This fake memory overhead continues in 'ethp' and 'prcl', as those configurations are also using the Python wrapper. Efficient THP ~~~~~~~~~~~~~ THP 'always' enabled policy achieves 4.29% speedup but incurs 6.92% memory overhead. It achieves 37.50% speedup in best case, but 81.03% memory overhead in worst case. Interestingly, both the best and worst case are with 'splash2x/ocean_ncp'). The 2-lines implementation of data access monitoring based THP version ('ethp') shows 1.56% speedup and 0.80% memory overhead. In other words, 'ethp' removes 88.43% of THP memory waste while preserving 36.36% of THP speedup in total. In case of the 'splash2x/ocean_ncp', 'ethp' removes 84.35% of THP memory waste while preserving 27.46% of THP speedup. Proactive Reclamation ~~~~~~~~~~~~~~~~~~~~ As same to the original work, I use 'zram' swap device for this configuration. In total, our 1 line implementation of Proactive Reclamation, 'prcl', incurred 8.41% runtime overhead in total while achieving 5.83% system memory usage reduction. Nonetheless, as the memory usage is calculated with 'MemFree' in '/proc/meminfo', it contains the SwapCached pages. As the swapcached pages can be easily evicted, I also measured the residential set size of the workloads: rss.avg orig rec (overhead) thp (overhead) ethp (overhead) prcl (overhead) parsec3/blackscholes 591726.600 589827.600 (-0.32) 594031.000 (0.39) 590779.800 (-0.16) 289370.800 (-51.10) parsec3/bodytrack 32191.800 32147.600 (-0.14) 32203.800 (0.04) 32190.800 (-0.00) 28509.600 (-11.44) parsec3/canneal 840227.000 840745.400 (0.06) 836600.000 (-0.43) 837091.200 (-0.37) 800416.200 (-4.74) parsec3/dedup 1209377.600 1235142.800 (2.13) 1236087.200 (2.21) 1235881.400 (2.19) 1040422.200 (-13.97) parsec3/facesim 311068.200 311408.800 (0.11) 315000.200 (1.26) 312630.200 (0.50) 189374.800 (-39.12) parsec3/ferret 99423.800 99612.800 (0.19) 100580.800 (1.16) 99643.800 (0.22) 89602.000 (-9.88) parsec3/fluidanimate 531795.200 531800.000 (0.00) 531768.200 (-0.01) 532213.800 (0.08) 404920.000 (-23.86) parsec3/freqmine 552705.200 553281.200 (0.10) 524774.600 (-5.05) 553188.000 (0.09) 75346.000 (-86.37) parsec3/raytrace 893993.000 894836.200 (0.09) 890009.400 (-0.45) 894002.800 (0.00) 370720.400 (-58.53) parsec3/streamcluster 110863.200 110800.800 (-0.06) 111202.800 (0.31) 111747.400 (0.80) 109678.400 (-1.07) parsec3/swaptions 5597.200 5565.600 (-0.56) 5579.000 (-0.33) 5581.000 (-0.29) 3895.000 (-30.41) parsec3/vips 31838.200 31800.600 (-0.12) 32053.800 (0.68) 32094.800 (0.81) 28568.400 (-10.27) parsec3/x264 81848.400 81968.200 (0.15) 82704.800 (1.05) 82349.600 (0.61) 80256.400 (-1.95) splash2x/barnes 1216330.000 1216704.800 (0.03) 1226790.600 (0.86) 1217759.800 (0.12) 652778.400 (-46.33) splash2x/fft 9604191.400 9478105.400 (-1.31) 9676396.200 (0.75) 9611581.200 (0.08) 8191631.600 (-14.71) splash2x/lu_cb 510556.200 510598.200 (0.01) 514475.400 (0.77) 510784.400 (0.04) 343677.000 (-32.69) splash2x/lu_ncb 509221.400 510098.000 (0.17) 513902.200 (0.92) 510608.800 (0.27) 348586.800 (-31.55) splash2x/ocean_cp 3385303.800 3388240.400 (0.09) 3446201.600 (1.80) 3396817.800 (0.34) 3194041.400 (-5.65) splash2x/ocean_ncp 3930791.200 3905177.000 (-0.65) 7180424.600 (82.67) 4491125.400 (14.26) 2764826.400 (-29.66) splash2x/radiosity 1474778.800 1475827.000 (0.07) 1485800.000 (0.75) 1473044.800 (-0.12) 337617.400 (-77.11) splash2x/radix 1766938.600 1753619.200 (-0.75) 1429421.200 (-19.10) 1745239.800 (-1.23) 1684783.200 (-4.65) splash2x/raytrace 23273.600 23279.200 (0.02) 28877.600 (24.08) 26965.400 (15.86) 14920.000 (-35.89) splash2x/volrend 44081.200 44069.600 (-0.03) 44206.800 (0.28) 44387.800 (0.70) 20929.600 (-52.52) splash2x/water_nsquared 29414.400 29396.000 (-0.06) 29877.400 (1.57) 29734.200 (1.09) 25908.800 (-11.92) splash2x/water_spatial 655966.800 656516.800 (0.08) 656515.200 (0.08) 656664.000 (0.11) 451478.000 (-31.17) total 28443600.000 28310500.000 (-0.47) 31525500.000 (10.84) 29033964.000 (2.08) 21542178.000 (-24.26) In total, 24.26% of residential sets were reduced. With parsec3/freqmine, 'prcl' reduced 86.37% of residential sets and 22% of system memory usage while incurring only 2.03% runtime overhead. Appendix F: Prototype Evaluations ================================= A prototype of DAMON has evaluated on an Intel Xeon E7-8837 machine using 20 benchmarks that picked from SPEC CPU 2006, NAS, Tensorflow Benchmark, SPLASH-2X, and PARSEC 3 benchmark suite. Nonethless, this section provides only summary of the results. For more detail, please refer to the slides used for the introduction of DAMON at the Linux Plumbers Conference 2019 or the MIDDLEWARE'19 industrial track paper.  SeongJae Park, Tracing Data Access Pattern with Bounded Overhead and Best-effort Accuracy. In The Linux Kernel Summit, September 2019. https://linuxplumbersconf.org/event/4/contributions/548/  SeongJae Park, Yunjae Lee, Heon Y. Yeom, Profiling Dynamic Data Access Patterns with Controlled Overhead and Quality. In 20th ACM/IFIP International Middleware Conference Industry, December 2019. https://dl.acm.org/doi/10.1145/3366626.3368125 Quality ------- We first traced and visualized the data access pattern of each workload. We were able to confirm that the visualized results are reasonably accurate by manually comparing those with the source code of the workloads. To see the usefulness of the monitoring, we optimized 9 memory intensive workloads among them for memory pressure situations using the DAMON outputs. In detail, we identified frequently accessed memory regions in each workload based on the DAMON results and protected them with ``mlock()`` system calls by manually modifying the source code. The optimized versions consistently show speedup (2.55x in best case, 1.65x in average) under artificial memory pressures. We use cgroups for the pressure. Overhead -------- We also measured the overhead of DAMON. The upperbound we set was kept as expected. Besides, it was much lower (0.6 percent of the bound in best case, 13.288 percent of the bound in average). This reduction of the overhead is mainly resulted from its core mechanism called adaptive regions adjustment. Refer to 'Appendix D' for more detail about the mechanism. We also compared the overhead of DAMON with that of a straightforward periodic PTE Accessed bit checking based monitoring. DAMON's overhead was smaller than it by 94,242.42x in best case, 3,159.61x in average. The latest version of DAMON running with its default configuration consumes only up to 1% of CPU time when applied to realistic workloads in PARSEC3 and SPLASH-2X and makes no visible slowdown to the target processes.
next reply index Thread overview: 18+ messages / expand[flat|nested] mbox.gz Atom feed top 2020-05-05 11:07 SeongJae Park [this message] 2020-05-05 11:08 ` [PATCH v10 01/16] scripts/spelling: Add a few more typos SeongJae Park 2020-05-05 11:08 ` [PATCH v10 02/16] mm/page_ext: Export lookup_page_ext() to GPL modules SeongJae Park 2020-05-05 11:08 ` [PATCH v10 03/16] mm: Introduce Data Access MONitor (DAMON) SeongJae Park 2020-05-05 11:08 ` [PATCH v10 04/16] mm/damon: Implement region based sampling SeongJae Park 2020-05-05 11:08 ` [PATCH v10 05/16] mm/damon: Adaptively adjust regions SeongJae Park 2020-05-05 11:08 ` [PATCH v10 06/16] mm/damon: Split regions into 4 subregions if possible SeongJae Park 2020-05-07 8:28 ` SeongJae Park 2020-05-05 11:08 ` [PATCH v10 07/16] mm/damon: Apply dynamic memory mapping changes SeongJae Park 2020-05-05 11:08 ` [PATCH v10 08/16] mm/damon: Implement callbacks SeongJae Park 2020-05-05 11:08 ` [PATCH v10 09/16] mm/damon: Implement access pattern recording SeongJae Park 2020-05-05 11:08 ` [PATCH v10 10/16] mm/damon: Add debugfs interface SeongJae Park 2020-05-05 11:08 ` [PATCH v10 11/16] mm/damon: Add tracepoints SeongJae Park 2020-05-05 11:08 ` [PATCH v10 12/16] tools: Add a minimal user-space tool for DAMON SeongJae Park 2020-05-05 11:08 ` [PATCH v10 13/16] Documentation/admin-guide/mm: Add a document " SeongJae Park 2020-05-05 11:08 ` [PATCH v10 14/16] mm/damon: Add kunit tests SeongJae Park 2020-05-05 11:08 ` [PATCH v10 15/16] mm/damon: Add user space selftests SeongJae Park 2020-05-05 11:08 ` [PATCH v10 16/16] MAINTAINERS: Update for DAMON SeongJae Park
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