damon

DAMON News List

Below is a list of news around DAMON project. This list is not exhaustive but just a DAMON maintainer’s collection of news. If you find a news that should be added to this list, please let us know at sj@kernel.org and/or damon@lists.linux.dev. 2024 2024-02-27: DAMON user-space tool, DAMO, has downloaded from PyPI more than 2,000 times last month. 2024-02-21: Yet another academic paper exploring DAMON for tiered memory management will be presented at EuroSys 2024

DAMON-based System Optimization Guide

This document helps you estimating the amount of benefit that you could get from DAMON-based system optimizations, and describes how you could achieve it. Check The Signs No optimization can provide same extent of benefit to every case. Therefore you should first guess how much improvements you could get using DAMON. If some of below conditions match your situation, you could consider using DAMON. Low IPC and High Cache Miss Ratios.

DAMON Evaluation

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 93.38% of residential sets and 23.63% of system memory footprint while incurring only 1.22% runtime overhead in the best case (parsec3/freqmine).

Summary of DAMON Development in 2022

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.

Tests package for DAMON is released under GPL v2

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.

Opening a Showcase Website for DAMON

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

DAMON: Data Access Monitor

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 accurate (the monitoring output is useful enough for DRAM level memory management; It might not be appropriate for CPU Cache levels, though), light-weight (the monitoring overhead is low enough to be applied online), and scalable (the upper-bound of the overhead is in constant range regardless of the size of target workloads).