On 02.01.2018 15:22, Jerome Kieffer wrote:
> On Tue, 02 Jan 2018 15:37:16 +0000
> Yasunori Endo <jo7...@gmail.com> 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 initiative already exists ... something which merges the
> approach of cuda and opencl but I have no idea on the momentum behind
> it.
>
>> Are there other reason not to support GPU on NumPy?
> yes. Matlab has such support and the performances gain are in the order
> of 2x vs 10x when addressing the GPU directly. All the time is spent in
> sending data back & forth. Numba is indeed a good candidate bu limited
> to the PTX assembly (i.e. cuda, hence nvidia hardware)
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 implementations to minimize data movement.
Given NumPy's current raw-pointer C API this seems difficult to
implement, though, as it is very hard to track memory aliases.
Regards,
Stefan
--
...ich hab' noch einen Koffer in Berlin...
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