gpu-memory-tracker

Tracks GPU memory usage over time as your simulation code is running

Usage

The code consists of two main scipts, nvml.py and nvml_reader.py, accompanied by the plotting module horizontal_bars_figure.py.

To launch the tracker:

$ pixi run tracker &

This will run in the background and append GPU usage statistics to a .csv file once per minute.

To plot the results from the .csv file:

$ pixi run reader -f nvml_20230531-214237.csv -s "2023-06-01 17:14" -e "2023-06-02 09:38"

where you replace the filename, start and end times accordingly.

Sample output:

                                         used_power_W                        used_gpu_memory_MiB
                                                  max        mean        std                 max          mean          std
gpu_uuid
GPU-0435bc0c-f6d1-aea8-c293-34dc683f3bbf          367  224.735926  85.705688               24501  21163.419652  1200.555189
GPU-1dfe3b5c-79a0-0422-f0d0-b22c6ded0af0          380  210.232344  93.814750               23831  20185.794268  1593.391977
GPU-1e8ab7c4-1892-d5b2-cff3-27ed68ab0cb0          371  203.268168  90.280413               25917  19064.484135  1914.924620
GPU-286afb69-cbe3-16d4-845a-95a44a732457          393  217.172979  94.241622               25375  20068.111566  2770.361860
GPU-3c1c67ff-efbc-8c7a-81fa-5a9fa1aacb6e          369  208.854383  94.394624               23939  17514.416588  3723.680108
GPU-607abfe7-dde7-bbde-e7dc-27df0c083cb3          386  217.731857  98.854606               23349  17429.276155  3812.925625
GPU-618947d4-f719-3af8-6f9a-5ad6c10717d9          383  213.703110  99.887138               23893  16419.121583  4225.272359
GPU-8903bbec-70a5-4259-bb82-c5f95d417bfe          381  209.343544  99.241505               23491  16424.200754  3968.190983
GPU-91cacd85-11ff-b3d6-ce9e-3035c49f91e0          392  229.511310  90.439730               24775  19014.547597  2374.283574
GPU-c55b3036-d54d-a885-7c6c-4238840c836e          402  230.864279  98.721442               25533  17333.577757  4708.808932
GPU-c7ebcb70-c0f3-376a-7d63-abfd5e4786e5          390  217.672479  95.850695               23237  17096.426956  3725.197609
GPU-cd37a5b2-e72d-eaa0-231c-f7abadbf1361          379  208.976437  95.956346               22979  16674.802074  3792.661263
GPU-ce697126-4112-a696-ff6b-1b072cdf03a2          379  220.402922  92.214866               22715  17681.816211  2745.141949
GPU-d0a28ddd-b3cf-6fb4-72c0-87158ddadd6e          374  216.918473  90.321331               24091  18213.516494  2847.337000
GPU-db57bc49-759c-283b-2e19-09c1a23d2676          385  218.780867  93.981904               23189  17615.459943  2504.511867
GPU-fd00ec2b-bc85-ee6f-cf6a-a92103974651          381  216.900094  94.578045               23981  17681.326107  3285.807881
nvml_20230531-214237.csv was recorded over the time interval from 2023-05-31 21:43:37.124133 to 2023-06-02 09:37:46.624458.

Running the reader will also generate two plots, one for memory and another for power usage over time. Here is an example for the fbpic PIC code running on 16 NVIDIA V100 GPUs:

Note: By default only python processes are tracked, this can be changed inside nvml.py.

Installation

We make use of the excellent pixi package manager, which can be installed on Linux/macOS via

$ curl -fsSL https://pixi.sh/install.sh | bash

One can then clone this repo via

$ git clone [email protected]:berceanu/gpu-memory-tracker.git