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Excerpt

Graphical Processing Units (GPUs) are currently one of the most popular devices for accelerating scientific computing. CUDA is currently one of the most popular languages to write code for efficient execution on NVIDIA GPUs, such as those in Topaz or GarrawarlaGarrawarla. For the AMD GPUs in Setonix, refer to the HIP page.

A very short introduction to GPU programming

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The code can be executed in an interactive SLURM session or within a batch job. An explicit request to use one or more GPUs is required. Terminal 1 shows an interactive session on TopazGarrawarla:

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Code Block
languagebash
themeDJango
titleTerminal 1. Run CUDA code in an ineractive session
$ salloc -N 1 -n 1 --gres=gpu:1 -p gpuq-dev -t 0:01:00

$ module load cuda/<version>

$ srun -n 1 --gres=gpu:1 --export=all ./helloCUDA
Hello from GPU thread 0 in block 0
Hello from GPU thread 1 in block 0
Hello from GPU thread 2 in block 0
Hello from GPU thread 3 in block 0
Hello from GPU thread 4 in block 0
Hello from GPU thread 0 in block 1
Hello from GPU thread 1 in block 1
Hello from GPU thread 2 in block 1
Hello from GPU thread 3 in block 1
Hello from GPU thread 4 in block 1
Hello from GPU thread 0 in block 3
Hello from GPU thread 1 in block 3
Hello from GPU thread 2 in block 3
Hello from GPU thread 3 in block 3
Hello from GPU thread 4 in block 3
Hello from GPU thread 0 in block 2
Hello from GPU thread 1 in block 2
Hello from GPU thread 2 in block 2
Hello from GPU thread 3 in block 2
Hello from GPU thread 4 in block 2


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