We are accelerating some matlab codes. We use a mix between gpuArrays and CUDA to achieve this. Does anyone have best practices document for that?
Does the gpu array include any clue to manage the number of blocks in a grid, the number of threads/block, shared memory, pinned memory, etc?
Dear Ahmed, this is a very general question. I have used the CUDA-Matlab integration for solving several types of problems. For instance, I have used it for solving large and sparse linear systems. Furthermore, I have used it for variable selection application in the context of multivariate calibration. In both cases, it was possible to demonstrate that the parallel version is faster than sequential one.
As far as I know, the Parallel Computing Toolbox (PCT) plugin still does not provide support for setting the number of blocks and threads per block. This is automatically defined by PCT in runtime through an optimized way.
I suggest you to read some papers of mine that describe this issue. Moreover, this link may be useful for you: https://www.ll.mit.edu/HPEC/agendas/proc07/Day3/11_Fatica_Poster.pdf
Best wishes and good luck with your research!
Hi, usually you are able to configure your Cuda kernel using the Matlab object CUDAKernel. See also http://de.mathworks.com/help/distcomp/cudakernel_object.html
Best Volkmar
http://de.mathworks.com/help/distcomp/cudakernel_object.html
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