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