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Re: Parallelization of Python on GPU?

References (1 earlier) <54ee8ce2$0$11109$c3e8da3@news.astraweb.com> <1424963166.30927.73.camel@gmail.com> <1915907417446661989.682673sturla.molden-gmail.com@news.gmane.org> <1424972883.30927.138.camel@gmail.com> <mco23o$edu$1@ger.gmane.org>
Date 2015-02-26 17:28 -0500
Subject Re: Parallelization of Python on GPU?
From Jason Swails <jason.swails@gmail.com>
Newsgroups comp.lang.python
Message-ID <mailman.19294.1424989738.18130.python-list@python.org> (permalink)

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On Thu, Feb 26, 2015 at 4:10 PM, Sturla Molden <sturla.molden@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...

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Thread

Parallelization of Python on GPU? John Ladasky <john_ladasky@sbcglobal.net> - 2015-02-25 18:35 -0800
  Re: Parallelization of Python on GPU? Steven D'Aprano <steve+comp.lang.python@pearwood.info> - 2015-02-26 14:02 +1100
    Re: Parallelization of Python on GPU? John Ladasky <john_ladasky@sbcglobal.net> - 2015-02-25 20:01 -0800
    Re: Parallelization of Python on GPU? Jason Swails <jason.swails@gmail.com> - 2015-02-26 10:06 -0500
    Re: Parallelization of Python on GPU? Sturla Molden <sturla.molden@gmail.com> - 2015-02-26 16:53 +0000
    Re: Parallelization of Python on GPU? Terry Reedy <tjreedy@udel.edu> - 2015-02-26 12:16 -0500
    Re: Parallelization of Python on GPU? Jason Swails <jason.swails@gmail.com> - 2015-02-26 12:48 -0500
    Re: Parallelization of Python on GPU? Sturla Molden <sturla.molden@gmail.com> - 2015-02-26 22:10 +0100
    Re: Parallelization of Python on GPU? Jason Swails <jason.swails@gmail.com> - 2015-02-26 17:28 -0500
  Re: Parallelization of Python on GPU? Ethan Furman <ethan@stoneleaf.us> - 2015-02-25 19:03 -0800
  Re: Parallelization of Python on GPU? Ethan Furman <ethan@stoneleaf.us> - 2015-02-25 19:05 -0800
    Re: Parallelization of Python on GPU? John Ladasky <john_ladasky@sbcglobal.net> - 2015-02-25 21:53 -0800
      Re: Parallelization of Python on GPU? Christian Gollwitzer <auriocus@gmx.de> - 2015-02-27 19:55 +0100
  Re: Parallelization of Python on GPU? Jason Swails <jason.swails@gmail.com> - 2015-02-26 10:27 -0500
  Re: Parallelization of Python on GPU? Sturla Molden <sturla.molden@gmail.com> - 2015-02-26 16:40 +0000
    Re: Parallelization of Python on GPU? John Ladasky <john_ladasky@sbcglobal.net> - 2015-02-26 09:34 -0800
      Re: Parallelization of Python on GPU? Sturla Molden <sturla.molden@gmail.com> - 2015-02-26 21:54 +0100

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