Recommended way to utilize GPUs via OpenCL, ROCm

classic Classic list List threaded Threaded
5 messages Options
Reply | Threaded
Open this post in threaded view
|

Recommended way to utilize GPUs via OpenCL, ROCm

Pankaj Jangid
Is there an officially recommended way to utilize AMD GPUs via OpenCL,
ROCm?

I came across ROCm website https://rocm.github.io/. This has Tensorflow
and PyTorch versions for using AMD GPUs. Just wanted to know if there is
a way to use my AMD GPUs for NumPy calculations.

--
Regards,
Pankaj Jangid
_______________________________________________
NumPy-Discussion mailing list
[hidden email]
https://mail.python.org/mailman/listinfo/numpy-discussion
Reply | Threaded
Open this post in threaded view
|

Re: Recommended way to utilize GPUs via OpenCL, ROCm

Hameer Abbasi
Hello Pankaj,

There's ClPy for OpenCL: https://github.com/fixstars/clpy
Also this pull request for CuPy (merged, but as yet unreleased): https://github.com/cupy/cupy/pull/1094

Best regards,
Hameer Abbasi

´╗┐On 18.10.19, 12:53, "NumPy-Discussion on behalf of Pankaj Jangid" <numpy-discussion-bounces+einstein.edison=[hidden email] on behalf of [hidden email]> wrote:

    Is there an officially recommended way to utilize AMD GPUs via OpenCL,
    ROCm?
   
    I came across ROCm website https://rocm.github.io/. This has Tensorflow
    and PyTorch versions for using AMD GPUs. Just wanted to know if there is
    a way to use my AMD GPUs for NumPy calculations.
   
    --
    Regards,
    Pankaj Jangid
    _______________________________________________
    NumPy-Discussion mailing list
    [hidden email]
    https://mail.python.org/mailman/listinfo/numpy-discussion
   
_______________________________________________
NumPy-Discussion mailing list
[hidden email]
https://mail.python.org/mailman/listinfo/numpy-discussion
Reply | Threaded
Open this post in threaded view
|

Re: Recommended way to utilize GPUs via OpenCL, ROCm

Pankaj Jangid
Hameer Abbasi <[hidden email]> writes:

> There's ClPy for OpenCL: https://github.com/fixstars/clpy
> Also this pull request for CuPy (merged, but as yet unreleased): https://github.com/cupy/cupy/pull/1094
>
This is great hope. Thanks for sharing this.

I wonder why NVIDIA's approach is so widely accepted. Sometimes, I
regret purchasing AMD GPUs. Not much support for them.

--
Regards,
Pankaj Jangid
_______________________________________________
NumPy-Discussion mailing list
[hidden email]
https://mail.python.org/mailman/listinfo/numpy-discussion
Reply | Threaded
Open this post in threaded view
|

Re: Recommended way to utilize GPUs via OpenCL, ROCm

Juan Nunez-Iglesias-2
I have also used PyOpenCL quite profitably:


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.

On 19 Oct 2019, at 3:39 am, Pankaj Jangid <[hidden email]> 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!

Juan.

_______________________________________________
NumPy-Discussion mailing list
[hidden email]
https://mail.python.org/mailman/listinfo/numpy-discussion
Reply | Threaded
Open this post in threaded view
|

Re: Recommended way to utilize GPUs via OpenCL, ROCm

Pankaj Jangid
Juan Nunez-Iglesias <[hidden email]> 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
hand.

Let me explore it further. Thanks for sharing.

>> On 19 Oct 2019, at 3:39 am, Pankaj Jangid <[hidden email]> 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. :-(

Regards,
--
Pankaj Jangid
_______________________________________________
NumPy-Discussion mailing list
[hidden email]
https://mail.python.org/mailman/listinfo/numpy-discussion