GPGPU |
General-Purpose Computation Using Graphics Hardware
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IntroductionGPGPU stands for General-Purpose computation on GPUs. With the increasing programmability of commodity graphics processing units (GPUs), these chips are capable of performing more than the specific graphics computations for which they were designed. They are now capable coprocessors, and their high speed makes them useful for a variety of applications. The goal of this page is to catalog the current and historical use of GPUs for general-purpose computation.
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NVIDIA and Addison-Wesley Release GPU Gems 3 Book
GPU Gems 3
 , the third volume of the best-selling GPU Gems series provides a snapshot of today's latest Graphics Processing Unit (GPU) programming techniques. The programmability of modern GPUs allows developers to not only distinguish themselves from one another but also to use this awesome processing power for non-graphics applications, such as physics simulation, financial analysis, and even virus detection—particularly with the CUDA architecture. Graphics remains the leading application for GPUs, and readers will find that the latest algorithms create ultra-realistic characters, better lighting, and post-rendering compositing effects. This third volume is certain to appeal to not just the many fans of the first two, but a whole new group of programmers as well.
(GPU Gems 3 Page at Addison-Wesley)
Posted: 10 Sep 2007 [GPGPU /Miscellaneous/Books] # Genome Technology Article about GPGPU: "Not Just for Kids Anymore" This article at Genome Technology gives a brief overview of GPGPU, with a focus on biological information processing using NVIDIA CUDA Technology. The article discusses the results from UIUC's NAMD / VMD project and neurological simulation company Evolved Machines.
Posted: 10 Sep 2007 [GPGPU /Med & Bio] # This paper by Anderson et al at Caltech describes a method to use GPUs to accelerate Quantum Monte Carlo on a GPU. QMC is among the most
accurate (and expensive) methods in the quantum chemistry zoo. Primarily, this involves the investigation of tricks available to this
algorithm to speed up matrix multiplication. That is, as a statistical algorithm, the authors studied the performance enhancements available when
multiplying many matrices simultaneously. Additionally, the paper explores the Kahan Summation Formula to improve the accuracy of GPU
matrix multiplication. (Quantum Monte Carlo on Graphical Processing Units. Amos G. Anderson, William A Goddard III, Peter Schroder. Computer Physics Communications)
Posted: 10 Sep 2007 [GPGPU /Scientific Computing] # |
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