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Optimizing MapReduce for GPUs with effective shared memory usage
2012
Proceedings of the 21st international symposium on High-Performance Parallel and Distributed Computing - HPDC '12
Accelerators and heterogeneous architectures in general, and GPUs in particular, have recently emerged as major players in high performance computing. For many classes of applications, MapReduce has emerged as the framework for easing parallel programming and improving programmer productivity. There have already been several efforts on implementing MapReduce on GPUs. In this paper, we propose a new implementation of MapReduce for GPUs, which is very effective in utilizing shared memory, a small
doi:10.1145/2287076.2287109
dblp:conf/hpdc/ChenA12
fatcat:oi75yvs57zhqpjx5iaio4pa4d4