论文标题
当地意识到的布鲁克·奥尔格瑟
A Locality-Aware Bruck Allgather
论文作者
论文摘要
集体算法是MPI的重要组成部分，允许应用程序程序员利用公共分布式操作的基本优化。 MPI_ALLGATHER收集最初分布在所有过程中的数据，因此所有数据均可用于每个过程。对于小数据尺寸，通常实现了布鲁克算法，以最大程度地减少任何过程传达的消息数量。但是，每个交流步骤的成本取决于源和目标过程的相对位置，以及非本地消息（例如节点）的成本，比本地消息（例如节点内的节点）要高得多。本文以局部意识优化了布鲁克算法，最大程度地降低了非本地消息的数量和大小，以提高Allgather操作的性能和可扩展性
Collective algorithms are an essential part of MPI, allowing application programmers to utilize underlying optimizations of common distributed operations. The MPI_Allgather gathers data, which is originally distributed across all processes, so that all data is available to each process. For small data sizes, the Bruck algorithm is commonly implemented to minimize the maximum number of messages communicated by any process. However, the cost of each step of communication is dependent upon the relative locations of source and destination processes, with non-local messages, such as inter-node, significantly more costly than local messages, such as intra-node. This paper optimizes the Bruck algorithm with locality-awareness, minimizing the number and size of non-local messages to improve performance and scalability of the allgather operation
