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> cp /opt/shared/gpucomputingsdk/4.0.17/C/bin/linux/release/simpleStreams .
> module load cuda
> bsub -q short_gpu -Ip -R "rusage [n_gpu_jobs=1 ]" ./simpleStreamsFor example, to run Matlab and access GPU resources:
> module load matlab
> bsub -q short_gpu -Ip -R "rusage [n_gpu_jobs=1 ]" matlab
The name of the compiler is nvcc and other tools can be found in:
/opt/shared/cudatoolkit/4.0.17/cuda/bin
The nvcc help file is obtained:
> nvcc -h
Also, you can view local cuda pdf docs on the cluster:
> evince /opt/shared/gpucomputingsdk/4.0.17/C/doc/programming_guide/CUDA_C_Programming_Guide.pdf
Matlab's Parallel Toolbox GPU demonstration applications is an excellent introduction. Additional applications such as Mathematica, Ansys, Maple and others offer various levels of support within their product. Bsub usage would be similar.
For example, to run Matlab and access GPU resources:
> module load matlab
> bsub -q short_gpu -Ip -R "rusage [n_gpu_jobs=1 ]" matlab
Additional GPU resources
There are many Cuda programming resources on the web and of course the Nvidia Cuda website.
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