Difference between revisions of "GPU job submission"

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== Example ==
== Example ==
* [[Neural network training with TensorFlow on GPU]]
* [[Neural network training with TensorFlow on GPU | Deep learning with TensorFlow on GPU]]
== Further reading ==
== Further reading ==

Revision as of 09:34, 19 August 2021

< Submit a parallel job


Monitor a job >

ⓘ Note

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

Cpu gpu system arch.png

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

To use the GPUs for a job node you need to request the ngpus_excl_p resource. It refers to the number of GPUs per node. This is unlike other resources, which are requested per core.

For example, to run a serial job with one GPU,

$ bsub -R "rusage[ngpus_excl_p=1]" ./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 gpu_mtotal0 host selection to do this. For example, if you need 10 GB (=10240  MB) per GPU:

 $ bsub -R "rusage[ngpus_excl_p=1]" -R "select[gpu_mtotal0>=10240]" ./my_cuda_program

This ensures your job will not run on GPUs with less than 10 GB of GPU memory.

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 -R "select[gpu_model1==GPU_MODEL]" resource requirement to bsub,

$ bsub -R "rusage[ngpus_excl_p=1]" -R "select[gpu_model0==GeForceGTX1080]" ./my_cuda_program

While your jobs will see all GPUs, LSF will set the CUDA_VISIBLE_DEVICES environment variable, which is honored by CUDA programs.

Available GPU node types

GPU Model Specifier GPU memory per GPU CPU cores per node CPU memory per node
NVIDIA GeForce GTX 1080 GeForceGTX1080 8 GiB 20 256 GiB
NVIDIA GeForce GTX 1080 Ti GeForceGTX1080Ti 11 GiB 20 256 GiB
NVIDIA GeForce RTX 2080 Ti GeForceRTX2080Ti 11 GiB 36 384 GiB
NVIDIA GeForce RTX 2080 Ti GeForceRTX2080Ti 11 GiB 128 512 GiB
NVIDIA Tesla V100-SXM2 32 GB TeslaV100_SXM2_32GB 32 GiB 48 768 GiB
NVIDIA Tesla A100 A100_PCIE_40GB 40 GiB 48 768 GiB


Further reading

< Submit a parallel job


Monitor a job >