MRCUDA: MapReduce Acceleration Framework Based on GPU

Jie Wang, Yanshuo Yu, Hang Cui, Shenglai Yang
School of Software Technology, Dalian University of Technology, Dalian 116024, China
Journal of Computational Information Systems 11: 7, 2615-2622, 2015


   title={MRCUDA: MapReduce Acceleration Framework Based on GPU},

   author={WANG, Jie and YU, Yanshuo and CUI, Hang and YANG, Shenglai},

   journal={Journal of Computational Information Systems},






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GPU programming model for general purpose computing is complex and difficult to be maintained. A MapReduce acceleration framework named MRCUDA is designed and implemented in this paper. There are four loosely coupled stages in MRCUDA, including Pre-Processing, Map, Group and Reduce, which can support flexible configurations for different applications. In order to take full advantage of GPU parallelism, a bitonic sorting algorithm is designed and implemented in the Group stage, and its performance is superior to general GPU sorting algorithms. Finally, according to five kinds of typical application tests, it is demonstrated that MRCUDA computing platform can reduce code scale and achieve ideal running speedup ratio.
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