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| Title | GPU Computing
(Article) |
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| in | Proceedings of the IEEE |
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| Author(s) |
John D. Owens, Mike Houston, David Luebke, Simon Green, John E. Stone, James C. Phillips |
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| Keyword(s) | GPGPU, GPU computing, parallel computing |
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| Year |
May 2008
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| Volume | 96 |
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| Number | 5 |
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| Pages | 879--899 |
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| BibTeX |  |
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| Abstract |
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 \emph{general-purpose computing on the GPU} (GPGPU), also
known as \emph{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.
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| Note |
Article is copyright IEEE, 2008.
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