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This post has migrated to https://damonitor.github.io/posts/damon_evaluation. This out-dated post will be removed soon.
DAMON is lightweight. It increases system memory usage by 0.39% and slows target workloads down by 1.16%.
DAMON is accurate and useful for memory management optimizations. An experimental DAMON-based operation scheme for THP, namely ‘ethp’, removes 76.15% of THP memory overheads while preserving 51.25% of THP speedup. Another experimental DAMON-based ‘proactive reclamation’ implementation, namely ‘prcl’, reduces 93.
A summary of DAMON development in 2022 has posted: https://lore.kernel.org/damon/20221229171209.162356-1-sj@kernel.org/
2022 was a year of active and healthy DAMON development.
Seven new DAMON major features were delivered to users. Some of those were featured in articles and academic papers.
It was possible thanks to the DAMON community. The community has expanded with its own mailing list and an open bi-weekly chat series. 40 people contributed their great code to DAMON via making their 275 commits merged into the mainline.
I will present current status and future plans for DAMON in KernelSummit'22. The title of the talk is “Current Status and Future Plans of DAMON”.
https://lpc.events/event/16/contributions/1224/
I will present DAMON/DAMOS in KernelSummit'21. The title of the talk is “Writing a fine-grained access pattern-oriented lightweight kernel module using DAMON/DAMOS in 10 minutes”.
https://linuxplumbersconf.org/event/11/contributions/984/
I realized I didn’t introduce a good, intuitive example use case of DAMON[0] for profiling so far, though DAMON is not for only profiling. One straightforward and realistic usage of DAMON as a profiling tool would be recording the monitoring results with callstack and visualize those by timeline together.
For example, below shows that visualization for a realistic workload, namely ‘fft’ in SPLASH-2X benchmark suite. The upper-most graph shows how DAMON-detected working set size of the workload (y-axis) changes by time (x-axis).
DAMON contains a number of tests based on the kselftest and kunit in its patchset itself. As it is preferred to contain only tests having short runtime in kernel tree, I organized time consuming tests in a package and used it in my company only. Tests could be used as a good document and essential for contributors. For the reason, I promised I will make it open source in the last kernel summit talk (https://linuxplumbersconf.
A DAMON showcase website[1] is open. There are
the official documentation of DAMON[2], the heatmap format dynamic access pattern of various realistic workloads for heap area[3], mmap()-ed area[4], and stack[5] area, the dynamic working set size distribution[6] and chronological working set size changes[7], and the latest performance test results[8]. [1] https://damonitor.github.io
[2] https://damonitor.github.io/doc/html/latest
[3] https://damonitor.github.io/test/result/visual/latest/heatmap.0.html
[4] https://damonitor.github.io/test/result/visual/latest/heatmap.1.html
[5] https://damonitor.github.io/test/result/visual/latest/heatmap.2.html
[6] https://damonitor.github.io/test/result/visual/latest/wss_sz.html
[7] https://damonitor.github.io/test/result/visual/latest/wss_time.html
[8] https://damonitor.github.io/test/result/perf/latest/html/index.html
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This post has migrated to https://damonitor.github.io/posts/damon. This out-dated post will be removed soon.
With increasingly data-intensive workloads and limited DRAM capacity, optimal memory management based on dynamic access patterns is becoming increasingly important. Such mechanisms are only possible if accurate and efficient dynamic access pattern monitoring is available.
DAMON is a Data Access MONitoring framework subsystem for the Linux kernel developed for such memory management. It is designed with some key mechanism (refer to Design for the detail) that make it