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 <ste...@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... > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@python.org > https://mail.python.org/mailman/listinfo/numpy-discussion > >
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