[Numpy-discussion] Direct GPU support on NumPy

Matthew Harrigan harrigan.matthew at gmail.com
Tue Jan 2 20:35:38 EST 2018

Is it possible to have NumPy use a BLAS/LAPACK library that is GPU
accelerated for certain problems?  Any recommendations or readme's on how
that might be set up?  The other packages are nice but I would really love
to just use scipy/sklearn and have decompositions, factorizations, etc for
big matrices go a little faster without recoding the algorithms.  Thanks

On Tue, Jan 2, 2018 at 5:04 PM, Stefan Seefeld <stefan at seefeld.name> wrote:

> On 02.01.2018 16:36, Matthieu Brucher wrote:
> Hi,
> Let's say that Numpy provides a GPU version on GPU. How would that work
> with all the packages that expect the memory to be allocated on CPU?
> It's not that Numpy refuses a GPU implementation, it's that it wouldn't
> solve the problem of GPU/CPU having different memory. When/if nVidia
> decides (finally) that memory should be also accessible from the CPU (like
> AMD APU), then this argument is actually void.
> I actually doubt that. Sure, having a unified memory is convenient for the
> programmer. But as long as copying data between host and GPU is orders of
> magnitude slower than copying data locally, performance will suffer.
> Addressing this performance issue requires some NUMA-like approach, moving
> the operation to where the data resides, rather than treating all data
> locations equal.
> [image: Stefan]
> --
>       ...ich hab' noch einen Koffer in Berlin...
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