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GPU Computing

John D. Owens,Mike Houston,David Luebke,Simon Green,John E. Stone,James C. Phillips
Proceedings of the IEEEIn Proceedings of the IEEE, Vol. 96, No. 5. (May 2008), pp. 879-899

@article{owens2008gpu,

   title={GPU computing},

   author={Owens, J.D. and Houston, M. and Luebke, D. and Green, S. and Stone, J.E. and Phillips, J.C.},

   journal={Proceedings of the IEEE},

   volume={96},

   number={5},

   pages={879–899},

   year={2008},

   publisher={IEEE}

}

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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.
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