Towards Efficient MapReduce Using MPI

  • Authors:
  • Torsten Hoefler;Andrew Lumsdaine;Jack Dongarra

  • Affiliations:
  • Open Systems Lab, Indiana University, Bloomington, USA;Open Systems Lab, Indiana University, Bloomington, USA;Department of Computer Science, University of Tennessee Knoxville,

  • Venue:
  • Proceedings of the 16th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface
  • Year:
  • 2009

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Abstract

MapReduce is an emerging programming paradigm for data-parallel applications. We discuss common strategies to implement a MapReduce runtime and propose an optimized implementation on top of MPI. Our implementation combines redistribution and reduce and moves them into the network . This approach especially benefits applications with a limited number of output keys in the map phase. We also show how anticipated MPI-2.2 and MPI-3 features, such as MPI_Reduce_local and nonblocking collective operations, can be used to implement and optimize MapReduce with a performance improvement of up to 25% on 127 cluster nodes. Finally, we discuss additional features that would enable MPI to more efficiently support all MapReduce applications.