Moim: A Multi-GPU MapReduce Framework

Mengjun Xie, Kyoung-Don Kang, Can Basaran
Department of Computer Science, State University of New York at Binghamton
IEEE International Symposium on MapReduce and Big Data Infrastructure, 2013

   title={Moim: A Multi-GPU MapReduce Framework},

   author={Xie, Mengjun and Kang, Kyoung-Don and Basaran, Can},



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MapReduce greatly decrease the complexity of developing applications for parallel data processing. To considerably improve the performance of MapReduce applications, we design a new MapReduce framework, called Moim, which 1) effectively utilizes both CPUs and GPUs (general purpose Graphics Processing Units), 2) overlaps CPU and GPU computations, 3) enhances load balancing in the map and reduce phases, and 4) efficiently handles not only fixed but also variable size data. We have implemented Moim and compared its performance with an advanced multi-GPU MapReduce framework. Moim achieves 20%-90% speedup for different data sizes and numbers of the GPUs used for data processing.
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