Simple GPU Example

Run deviceQuery (from NVIDIA SDK) on one device:

MPI + GPU Jobs

To run an MPI application on the GPU nodes, you need to take special care to correctly set the numer of MPI ranks per node.  Typical setups are:

1. one process per node (ppn):

   1. If you would like to use only one GPU per node (Why? For exclusive jobs you waste the ressources of second GPU, and even in non-exclusive case you should try to pack two of your MPI ranks on same node to reduce the amount of nodes); or

   2. If your process uses both GPUs at the same time, e.g. via cudaSetDevice or by accepting CUDA_VISIBLE_DEVICES (set automatically by the batch system):

      gpu_batch_4n_1r_2g.sh

      #!/usr/local_rwth/bin/zsh   ### Setup in this script: ### - 4 nodes (c18g, default) ### - 1 rank per node ### - 2 GPUs per rank (= both GPUs from the node)   #SBATCH -J 4-1-2 #SBATCH -o 4-1-2.%J.log #SBATCH --ntasks=4 #SBATCH --ntasks-per-node=1 #SBATCH --gres=gpu:2   module load cuda   #print some debug informations... echo; export; echo;  nvidia-smi; echo   $MPIEXEC $FLAGS_MPI_BATCH ./cuda-mpi -g 2

2. two processes per node (ppn):

   1. If each process communicates to a own single GPU and thus using both GPUs on a node (recommended setup):

       

      gpu_batch_2n_2r_1g.sh

      #!/usr/local_rwth/bin/zsh   ### Setup in this script: ### - 2 nodes (c18g, default) ### - 2 ranks per node ### - 1 GPU per rank (= both GPUs from the node)   #SBATCH -J 2-2-1 #SBATCH -o 2-2-1.%J.log   #SBATCH --ntasks=4 #SBATCH --ntasks-per-node=2 #SBATCH --gres=gpu:2   module load cuda   #print some debug informations... echo; export; echo;  nvidia-smi; echo   $MPIEXEC $FLAGS_MPI_BATCH ./cuda-mpi

3. More than 2 processes per node:

   1. If you also have processes that do computation on the CPU only.

Information about the accounting of SLURM can be found in SLURM Accounting.

Information about Submitting a GPU job can be found in Submitting a GPU job.