[Numpy-discussion] Recommended way to utilize GPUs via OpenCL, ROCm

Pankaj Jangid pankaj.jangid at gmail.com
Mon Oct 21 06:27:11 EDT 2019

Juan Nunez-Iglesias <jni at fastmail.com> writes:

> I have also used PyOpenCL quite profitably:
> https://github.com/inducer/pyopencl <https://github.com/inducer/pyopencl>
> I philosophically prefer it to ROCm because it targets *all* GPUs, including intel integrated graphics on most laptops, which can actually get quite decent (30x) speedups.

This is a good find. There is some work involved but it is good. It
gives transparent access to underlying hardware. I wish NumPy operations
automatically use the available resources. That is more concise. It will
give scientific community an edge. I am not saying they are not good
programmers but still it will let them focus on the main problem at

Let me explore it further. Thanks for sharing.

>> On 19 Oct 2019, at 3:39 am, Pankaj Jangid <pankaj.jangid at gmail.com> wrote:
>> I wonder why NVIDIA's approach is so widely accepted. Sometimes, I
>> regret purchasing AMD GPUs. Not much support for them.
> I agree. I am very disappointed by the NVIDIA monopoly in scientific computing. Resist!
Really, very disappointing. :-(

Pankaj Jangid

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