Difference between revisions of "GPU Usage"
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Since we got 12 dedicated GPU nodes (mpcg[001-009]) containing one [https://www.nvidia.com/object/tesla-p100.html NVIDIA Tesla P100] each and four additional nodes (mpc[001-004]) containing two [https://www.nvidia.com/en-us/geforce/products/10series/geforce-gtx-1080/ GTX 1080] each, its possible to run your jobs with one or multiple associated GPUs. The usage might not be self-explanatory, we created this guide to help you get everything set up and working properly. | Since we got 12 dedicated GPU nodes (mpcg[001-009]) containing one [https://www.nvidia.com/object/tesla-p100.html NVIDIA Tesla P100] each and four additional nodes (mpc[001-004]) containing two [https://www.nvidia.com/en-us/geforce/products/10series/geforce-gtx-1080/ GTX 1080] each, its possible to run your jobs with one or multiple associated GPUs. The usage might not be self-explanatory, we created this guide to help you get everything set up and working properly. | ||
=== Examples === | === Examples === | ||
Revision as of 10:44, 21 February 2018
Introduction
Since we got 12 dedicated GPU nodes (mpcg[001-009]) containing one NVIDIA Tesla P100 each and four additional nodes (mpc[001-004]) containing two GTX 1080 each, its possible to run your jobs with one or multiple associated GPUs. The usage might not be self-explanatory, we created this guide to help you get everything set up and working properly.
Examples
In order to use one or more GPUs for your Job you will have to request a Generic resource (GRES). You can do that by adding the following line to your jobscript:
#SBATCH --gres=gpu:1
This will request one GPU. A suitable node will be automatically chosen by SLURM.
