Hey Kevin, Not sure about the CUDA limitations, I'll let others speak to that...
But in developing the mne-python CUDA filtering code, IIRC the primary limitation was (by far) transferring the data to and from the GPU. The FFT computations themselves were a fraction of the total time. I suspect using multiple jobs won't help CUDA filtering very much since the jobs would presumably compete for the same memory bandwidth, but I would love to be wrong about this. If it works better, it would be great to open an mne-python issue for it, as we are always looking for speedups :) Cheers, Eric On Nov 1, 2014 7:21 PM, "kjs" <[email protected]> wrote: > Hello, > > I have written an MPI routine in Python that sends jobs to N worker > processes. The root process handles file IO and the workers do > computation. In the worker processes calls are made to the cuda enabled > GPU to do FFTs. > > Is it safe to have N processes potentially making calls to the same GPU > at the same time? I have not made any amendments to the cuda code[0], > and have little knowledge of what could possibly go wrong. > > Thanks much, > Kevin > > [0] I am using python-mne with cuda enabled to call scikits.cuda.fft > https://github.com/mne-tools/mne-python/blob/master/mne/cuda.py > > _______________________________________________ > PyCUDA mailing list > [email protected] > http://lists.tiker.net/listinfo/pycuda > >
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