Eric Larson wrote: > 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 >> >> >
Thanks Andreas, this is good to know. I noticed that even though pycuda is currently only using one of two GPUs, that GPU is only ever at ~35% memory and ~22% processing utilization. This could be related to Eric's observation that the PCI-e 16x bus bandwidth reaches capacity while the GPU is pushing out fast FFT'ed arrays. Thus allowing for only one or two arrays in the GPU at the same time. From what I have seen, using cuda speeds up my FFTs ~2x. Though, the workers do many other computations on the CPU. It's a worst case scenario that all N workers are trying to send data to the GPU at the same time. -Kevin
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