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Assessing the Performance and Scalability of a Novel Multilevel K-Nomial Allgather on CORE-Direct Systems

机译:评估新型多级K语群全能对核心直接系统的性能和可扩展性

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In this paper, we propose a novel allgather algorithm, Reindexed Recursive K-ing (RRK), which leverages flexibility in the algorithm's tree topology and ability to make asynchronous progress coupled with Core-Direct communication offload capability to optimize the MPI_Allgather for Core-Direct enabled systems. In particular, the RRK introduces a reindexing scheme which ensures contiguous data transfers while adding only a single additional send and receive operation for any radix, k, or communicator size, N. This allows us to improve algorithm scalability by avoiding the use of a scatter/gather elements (SGE) list on InfiniBand networks. The implementations of the RRK algorithm and its evaluation shows that it performs and scales well on Core-Direct systems for a wide range of message sizes and various communicator configurations.
机译:在本文中,我们提出了一种新的Altgerather算法,重新出现的递归k-ing(RRK),它利用算法的树拓扑结构和能力使异步进步与核心直接通信卸载能力进行了优化,以优化核心直接的MPI_allgather支持的系统。特别地,RRK介绍了一种重新填充方案,该方案确保了连续数据传输,同时仅增加了任何基数,k或通信器大小的单个附加发送和接收操作,这允许我们通过避免使用分散来提高算法可伸缩性/收集InfiniBand网络上的元素(SGE)列表。 RRK算法的实现及其评估表明它在核心直接系统上执行和缩放,用于广泛的消息大小和各种通信器配置。

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