GPU Computing

Вычисления на графическом процессоре
Michael Houston, J. C. Phillips, John E. Stone, David Luebke, John D. Owens, S. Green
2008-04-16

GPU computingcomputational biophysicsgeneral-purpose computing on the GPUhigh-performance computingparallel programmable processor
The graphics processing unit (GPU) has become an integral part of today's mainstream computing systems. Over the past six years, there has been a marked increase in the performance and capabilities of GPUs. The modern GPU is not only a powerful graphics engine but also a highly parallel programmable processor featuring peak arithmetic and memory bandwidth that substantially outpaces its CPU counterpart. The GPU's rapid increase in both programmability and capability has spawned a research community that has successfully mapped a broad range of computationally demanding, complex problems to the GPU. This effort in general-purpose computing on the GPU, also known as GPU computing, has positioned the GPU as a compelling alternative to traditional microprocessors in high-performance computer systems of the future. We describe the background, hardware, and programming model for GPU computing, summarize the state of the art in tools and techniques, and present four GPU computing successes in game physics and computational biophysics that deliver order-of-magnitude performance gains over optimized CPU applications.
1
GPU computing has emerged as a compelling alternative to traditional microprocessors for future high-performance computing systems.
2
Modern GPUs combine powerful graphics capabilities with highly parallel programmability, peak arithmetic throughput, and memory bandwidth that substantially exceed those of CPUs.
3
Rapid advances in GPU programmability and capabilities have enabled diverse computationally demanding problems to be effectively mapped onto GPUs.
4
The paper reviews GPU-computing hardware, programming models, tools, and techniques, and presents four successful applications in game physics and computational biophysics.
5
The reported GPU applications achieve order-of-magnitude performance gains compared with optimized CPU implementations.

GPU computing systems and applications

GPU hardware capabilities, programming models, tools, techniques, and performance advantages for general-purpose high-performance computing

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Publication Date
2008-04-16
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Authors
Michael Houston
J. C. Phillips
John E. Stone
David Luebke
John D. Owens
S. Green
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