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| 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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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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