Parallelization of Python on GPU?

[Jason Swails]

On Thu, Feb 26, 2015 at 4:10 PM, Sturla Molden <sturla.molden at gmail.com>
wrote:

> On 26/02/15 18:48, Jason Swails wrote:
>
>> On Thu, 2015-02-26 at 16:53 +0000, Sturla Molden wrote:
>>
>>> GPU computing is great if you have the following:
>>>
>>> 1. Your data structures are arrays floating point numbers.
>>>
>>
>> It actually works equally great, if not better, for integers.
>>
>
> Right, but not complicated data structures with a lot of references or
> pointers. It requires data are laid out in regular arrays, and then it acts
> on these arrays in a data-parallel manner. It is designed to process
> vertices in parallel for computer graphics, and that is a limitation which
> is always there. It is not a CPU with 1024 cores. It is a "floating point
> monster" which can process 1024 vectors in parallel. You write a tiny
> kernel in a C-like language (CUDA, OpenCL) to process one vector, and then
> it will apply the kernel to all the vectors in an array of vectors. It is
> very comparable to how GLSL and Direct3D vertex and fragment shaders work.
> (The reason for which should be obvious.) The GPU is actually great for a
> lot of things in science, but it is not a CPU. The biggest mistake in the
> GPGPU hype is the idea that the GPU will behave like a CPU with many cores.


Very well summarized.  At least in my field, though, it is well-known that
GPUs are not 'uber-fast CPUs'.  Algorithms have been redesigned, programs
rewritten to take advantage of their architecture.  It has been a *massive*
investment of time and resources, but (unlike the Xeon Phi coprocessor [1])
has reaped most of its promised rewards.

​--Jason

[1] I couldn't resist the jab.  At several times the cost of the top of the
line NVidia gaming card, the GPU is about 15-20x faster...
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/python-list/attachments/20150226/92c7fd1e/attachment.html>