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10.1109/PDCAT.2011.60guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
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Optimizing MPI Alltoall Communication of Large Messages in Multicore Clusters

Published: 20 October 2011 Publication History

Abstract

MPI All to all communication is widely used in many high performance computing (HPC) applications. In All to all communication, each process sends a distinct message to all other participating processes. In multicore clusters, processes within a node simultaneously contend for the same network resource of the node in All to all communication. However, many small synchronization messages are required in All to all communication of large messages. With the contention, their latency is orders of magnitude larger than that without contention. As a result, the synchronization overhead is significantly increased and accounts for a large proportion to the whole latency of All to all communication. In this paper, we analyse the considerable overhead of synchronization messages. Base on the analysis, an optimization is presented to reduce the number of synchronization messages from 3N to 2 N. Evaluations on a 240-core cluster show that the performance is improved by almost constant ratio, which is mainly determined by message size and independent of system scale. The performance of All to all communication is improved by 25% for 32K and 64K bytes messages. For FFT application, performance is improved by 20%.

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  • (2022)Applying on Node Aggregation Methods to MPI Alltoall Collectives: Matrix Block Aggregation AlgorithmProceedings of the 29th European MPI Users' Group Meeting10.1145/3555819.3555821(11-17)Online publication date: 14-Sep-2022

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      cover image Guide Proceedings
      PDCAT '11: Proceedings of the 2011 12th International Conference on Parallel and Distributed Computing, Applications and Technologies
      October 2011
      442 pages
      ISBN:9780769545646

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      IEEE Computer Society

      United States

      Publication History

      Published: 20 October 2011

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      • (2022)Applying on Node Aggregation Methods to MPI Alltoall Collectives: Matrix Block Aggregation AlgorithmProceedings of the 29th European MPI Users' Group Meeting10.1145/3555819.3555821(11-17)Online publication date: 14-Sep-2022

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