damo 1.3.2

DAMON user-space tool

DAMO: Data Access Monitoring Operator

damo is a user space tool for DAMON. Using this, you can monitor the data access patterns of your system or workloads and make data access-aware memory management optimizations.

Demo Video

Please click the below thumbnail to show the short demo video.

Getting Started

Follow below instructions and commands to monitor and visualize the access pattern of your workload.

$ # ensure your kernel is built with CONFIG_DAMON_*=y
$ sudo mount -t debugfs none /sys/kernel/debug/
$ sudo pip3 install damo
$ sudo damo record $(pidof <your workload>)
$ damo report heats --heatmap stdout --stdout_heatmap_color emotion

The last command will show the access pattern of your workload, like below:

FAQs

How can I install a kernel that is built with CONFIG_DAMON_*=y?

Please refer to 'Install' section of https://damonitor.github.io/.

Where can I get more detailed usage?

The below sections provide quick introductions for damo's major features. For more detailed usage, please refer to USAGE.md file.

What does the version number mean?

Nothing at all but indicate which version is more fresh. A higher version number means it is more recently released.

Will pip3 install damo installs the latest version of damo?

It will install the latest stable version of damo. If you want, you can also install less stable but more fresh damo from source code. For that, fetch the next branch of the source tree and use damo executable file in the tree.

$ git clone https://github.com/awslabs/damo -b next

How can I participate in the development of damo?

Please refer to CONTRIBUTING file.

Recording Data Access Patterns

Below commands record memory access patterns of a program and save the monitoring results in damon.data file.

$ git clone https://github.com/sjp38/masim
$ cd masim; make; ./masim ./configs/zigzag.cfg &
$ sudo damo record -o damon.data $(pidof masim)

The first two lines of the commands get an artificial memory access generator program and run it in the background. It will repeatedly access two 100 MiB-sized memory regions one by one. You can substitute this with your real workload. The last line asks damo to record the access pattern in damon.data file.

Visualizing Recorded Patterns

Below three commands visualize the recorded access patterns into three image files.

$ damo report heats --heatmap access_pattern_heatmap.png
$ damo report wss --range 0 101 1 --plot wss_dist.png
$ damo report wss --range 0 101 1 --sortby time --plot wss_chron_change.png

• access_pattern_heatmap.png will show the data access pattern in a heatmap, which shows when (x-axis) what memory region (y-axis) is how frequently accessed (color).
• wss_dist.png will show the distribution of the working set size.
• wss_chron_change.png will show how the working set size has chronologically changed.

You can show the images on a web page [1]. Those made with other realistic workloads are also available [2,3,4].

[1] https://damonitor.github.io/doc/html/latest/admin-guide/mm/damon/start.html#visualizing-recorded-patterns
[2] https://damonitor.github.io/test/result/visual/latest/rec.heatmap.1.png.html
[3] https://damonitor.github.io/test/result/visual/latest/rec.wss_sz.png.html
[4] https://damonitor.github.io/test/result/visual/latest/rec.wss_time.png.html

Data Access Pattern Aware Memory Management

Below three commands make every memory region of size >=4K that hasn't accessed for >=60 seconds in your workload to be swapped out. By doing this, you can make your workload more memory efficient with only a modest performance overhead.

$ echo "#min-size max-size min-acc max-acc min-age max-age action" > my_scheme
$ echo "4K        max      0       0       60s     max     pageout" >> my_scheme
$ sudo damo schemes -c my_scheme <pid of your workload>