Towards efficient mapreduce using MPI. Hoefler, T., Lumsdaine, A., & Dongarra, J. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5759 LNCS:240-249, 2009. ![Towards efficient mapreduce using MPI [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 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. © 2009 Springer Berlin Heidelberg.
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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. © 2009 Springer Berlin Heidelberg.},
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