For the list... On Wed, Aug 12, 2009 at 11:39 AM, M. Badawy<[email protected]> wrote: > From what I have read so far, it seems that using Matlab w/ > AccelerEyes' Jacket seems to be the fastest and easiest method to use > CUDA w/ Matlab. So, assuming that I'll be using Jacket, is there a > significant performance > difference between this option and using Pycuda?
I haven't used Jacket, maybe someone else can comment. There will not be any difference in the GPU computation whether you use mex or PyCUDA because the underlying kernels are in C anyway; but I think Jacket generates its own kernels so it might be faster or slower depending on how smart their compiler is, but at this stage, I suspect a compiler wouldn't be that much better at tuning a CUDA kernel than a human (might be a lot worse). I used PyCUDA with great success from Sage (then just Python with Numpy, etc.); then I wrote a mex interface to set up and call my kernels from Matlab. The latter took a lot more effort because you have to set up your device and convert your datastructures (e.g., structure-of-arrays to array-of-structures for complex numbers) and handle transfers and free memory manually in C. And SciPy can save data to Matlab-compatible .mat files anyway for good inter-compatibility. I also want my implementation to be open-source one day, and don't want to force my colleagues to have copies of Matlab and Jacket. _______________________________________________ PyCUDA mailing list [email protected] http://tiker.net/mailman/listinfo/pycuda_tiker.net
