Table of Contents
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The gpu_quad partition is open to any users working for a PI with a primary or secondary appointment in a pre-clinical department; to run jobs on the gpu_quad partition use the flag -p gpu_quad. If you work at an affiliate institution but are collaborating with an on-Quad PI, please contact Research Computing to gain access.
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login01:sbatch gpujob.sh Submitted batch job 6900310 where gpujob.sh contains #----------------------------------------------------------------------------------------- #!/bin/bash #SBATCH -c 4 #SBATCH -t 6:00:00 #SBATCH -p gpu_quad #SBATCH --gres=gpu:2 module load gcc/6.2.0 module load cuda/9.0 ./deviceQuery #this is just an example #----------------------------------------------------------------------------------------- |
It is also possible to request a specific type of GPU card by using the --gres flag. For example --gres=gpu:teslaM40:3 can be used to request 3 GPU Tesla M40 cards.
Currently the GPU flags available are: teslaK80, teslaM40, teslaV100, teslaV100s, rtx6000, rtx8000, a100 however each partitions might only have a subset of those card types, as indicated in the first paragraph.
It is also possible to request a minimum amount of VRAM available on the GPU card to be allocated for the job. This can be done using the gres vram. For example using the flag --gres=gpu:1,vram:15G would request a GPU card that has at least 15G of VRAM. To see the VRAM of each card type in O2 you can use the Slurm command sinfo -p gpu,gpu_quad,gpu_requeue --Format=nodehost,gres:40
How to compile and run Cuda programs
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