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As part of the recent research cluster summer 2011 upgrade, one compute node was provisioned with two Nvidia Tesla M2050 GPU processors. GPU processing is an excellent means to achieve shorter run times for many algorithms. There are two approaches to to use this resource. One is to program in Nvidia's programming language Cuda. The other approach is to use Matlab and other commercial applications that have GPU support.
Note, Nvidia Cuda and applications such as Matlab require specific coding to use gpu resources.
Note: Over time different versions of Cuda and sdk will change. Check the current versions available with the module command.
> module available
You'll find the CUDA toolkit in /opt/shared/cudatoolkit and the GPU computing SDK in /opt/shared/gpucomputingsdk. The SDK contains a number of CUDA sample C applications that can be found at /opt/shared/gpucomputingsdk/4.2.9/C. Compiled samples can be found in /opt/shared/gpucomputingsdk/4.2.9/C/bin/linux/release.
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