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Mars: a MapReduce framework on graphics processors

Bingsheng He, Wenbin Fang, Qiong Luo, Naga K. Govindaraju, Tuyong Wang
Hong Kong University of Science and Technology, Hong Kong
In PACT ’08: Proceedings of the 17th international conference on Parallel architectures and compilation techniques (2008), pp. 260-269.

@conference{he2008mars,

   title={Mars: a MapReduce framework on graphics processors},

   author={He, B. and Fang, W. and Luo, Q. and Govindaraju, N.K. and Wang, T.},

   booktitle={Proceedings of the 17th international conference on Parallel architectures and compilation techniques},

   pages={260–269},

   year={2008},

   organization={ACM}

}

We design and implement Mars, a MapReduce framework, on graphics processors (GPUs). MapReduce is a distributed programming framework originally proposed by Google for the ease of development of web search applications on a large number of commodity CPUs. Compared with CPUs, GPUs have an order of magnitude higher computation power and memory bandwidth, but are harder to program since their architectures are designed as a special-purpose co-processor and their programming interfaces are typically for graphics applications. As the first attempt to harness GPU’s power for MapReduce, we developed Mars on an NVIDIA G80 GPU, which contains over one hundred processors, and evaluated it in comparison with Phoenix, the state-of-the-art MapReduce framework on multi-core CPUs. Mars hides the programming complexity of the GPU behind the simple and familiar MapReduce interface. It is up to 16 times faster than its CPU-based counterpart for six common web applications on a quad-core machine.
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