> 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
If you have very big matrices, scikit-learn's PCA already uses randomized
linear algebra, w
On Jan 2, 2018 8:35 PM, "Matthew Harrigan"
wrote:
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 h
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 matr
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/
On Tue, Jan 2, 2018 at 1:21 PM, Yasunori Endo wrote:
>
> Hi all
>
> Numba looks so nice library to try.
> Thanks for the information.
>
>> This suggests a new, higher-level data model which supports replicating
data into different memory spaces (e.g. host and GPU). Then users (or some
higher layer
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 (fin
Hi all
Numba looks so nice library to try.
Thanks for the information.
This suggests a new, higher-level data model which supports replicating
> data into different memory spaces (e.g. host and GPU). Then users (or some
> higher layer in the software stack) can dispatch operations to suitable
> i
On 02.01.2018 15:22, Jerome Kieffer wrote:
> On Tue, 02 Jan 2018 15:37:16 +
> Yasunori Endo wrote:
>
>> If the reason is just about human resources,
>> I'd like to try implementing GPU support on my NumPy fork.
>> My goal is to create standard NumPy interface which supports
>> both CUDA and Op
On Tue, 02 Jan 2018 15:37:16 +
Yasunori Endo wrote:
> If the reason is just about human resources,
> I'd like to try implementing GPU support on my NumPy fork.
> My goal is to create standard NumPy interface which supports
> both CUDA and OpenCL, and more devices if available.
I think this i
On Tue, Jan 2, 2018 at 10:37 AM, Yasunori Endo wrote:
> Hi
>
> I recently started working with Python and GPU,
> found that there're lot's of libraries provides
> ndarray like interface such as CuPy/PyOpenCL/PyCUDA/etc.
> I got so confused which one to use.
>
> Is there any reason not to support G
Hi
I recently started working with Python and GPU,
found that there're lot's of libraries provides
ndarray like interface such as CuPy/PyOpenCL/PyCUDA/etc.
I got so confused which one to use.
Is there any reason not to support GPU computation
directly on the NumPy itself?
I want NumPy to support
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