GPU job submission with SLURM

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ⓘ Note

You can only use GPUs if you are a member of a shareholder group that invested into GPU nodes

Figure: Here is an example of CPU & GPU system architecture. There are several system architectures on the cluster.

To use the GPUs for a job node you need to request the -G or --gpus resource. For example, to run a serial job with one GPU : $ sbatch -G 1 --wrap="./my_cuda_program" How to select GPU memory If you know that you will need more memory on a GPU than some models provide, i.e., more than 8 GB, then you can request that your job will run only on GPUs that have enough memory. Use the --gres=gpumem: option to do this. For example, if you need 10 GB (=10240  MB) per GPU: $ sbatch -G 1 --gres=gpumem:10g --wrap="./my_cuda_program" This ensures your job will not run on GPUs with less than 10 GB of GPU memory. The default unit for the gpumem option is bytes. You are therefore advised to specify units, for example 20g or 11000m. How to select a GPU model In some cases it is desirable or necessary to select the GPU model on which your job runs, for example if you know you code runs much faster on a newer model. However, you should consider that by narrowing down the list of allowable GPUs, your job may need to wait for a longer time. To select a certain GPU model, add the --gpus=slurm_specifierl:N resource requirement to sbatch, where the SLURM specifier for the GPU models is detailed in the table below, and N is the number of requested GPUs. $ sbatch -G 1 --gpus=gtx_1080:1 --wrap="./my_cuda_program" programs.

Available GPU node types

GPU Model	LSF Specifier (GPU driver > 450.80.02)	Slurm specifier	GPU memory per GPU	CPU cores per node	CPU memory per node
NVIDIA GeForce GTX 1080	NVIDIAGeForceGTX1080	gtx_1080	8 GiB	20	256 GiB
NVIDIA GeForce GTX 1080 Ti	NVIDIAGeForceGTX1080Ti	gtx_1080_ti	11 GiB	20	256 GiB
NVIDIA GeForce RTX 2080 Ti	NVIDIAGeForceRTX2080Ti	rtx_2080_ti	11 GiB	36	384 GiB
NVIDIA GeForce RTX 2080 Ti	NVIDIAGeForceRTX2080Ti	rtx_2080_ti	11 GiB	128	512 GiB
NVIDIA GeForce RTX 3090	NVIDIAGeForceRTX3090	rtx_3090	24 GiB	128	512 GiB
NVIDIA TITAN RTX	NVIDIATITANRTX	titan_rtx	24 GiB	128	512 GiB
NVIDIA Quadro RTX 6000	QuadroRTX6000	quadro_rtx_6000	24 GiB	128	512 GiB
NVIDIA Tesla V100-SXM2 32 GiB	TeslaV100_SXM2_32GB	v100	32 GiB	48	768 GiB
NVIDIA Tesla V100-SXM2 32 GB	TeslaV100_SXM2_32GB	v100	32 GiB	40	512 GiB
Nvidia Tesla A100 (40 GiB)	NVIDIAA100_PCIE_40GB	a100-pcie-40gb	40 GiB	48	768 GiB
Nvidia Tesla A100 (80 GiB)	unavailable	a100_80gb	80 GiB	48	1024 GiB

Example

• Deep learning with TensorFlow on GPU

Further reading

• Getting started with GPUs

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