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RAHTM: Routing Algorithm Aware Hierarchical Task Mapping

机译:RAHTM:路由算法感知的分层任务映射

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摘要

The mapping of MPI processes to compute nodes on a supercomputer can have a significant impact on communication performance. For high performance computing (HPC) applications with iterative communication, rich offline analysis of such communication can improve performance by optimizing the mapping. Unfortunately, current practices for at-scale HPC consider only the communication graph and network topology in solving this problem. We propose Routing Algorithm aware Hierarchical Task Mapping (RAHTM) which leverages the knowledge of the routing algorithm to improve task mapping. RAHTM achieves high quality mappings by combining (1) a divide-and-conquer strategy to achieve scalability, (2) a limited search of mappings, and (3) a linear programming based routing-aware approach to evaluate possible mappings in the search space. RAHTM achieves 20% reduction in the communication time and 9% reduction in the overall execution time for three communication-heavy benchmarks scaled up to 16,384 processes on a Blue Gene/Q platform.
机译:MPI进程到超级计算机上的计算节点的映射可能会对通信性能产生重大影响。对于具有迭代通信的高性能计算(HPC)应用程序,此类通信的丰富脱机分析可以通过优化映射来提高性能。不幸的是,当前的大规模HPC实践在解决此问题时仅考虑通信图和网络拓扑。我们提出了路由算法感知的分层任务映射(RAHTM),它利用路由算法的知识来改进任务映射。 RAHTM通过结合(1)分而治之策略来实现可伸缩性,(2)有限的映射搜索以及(3)基于线性编程的路由感知方法来评估搜索空间中的可能映射来实现高质量的映射。在Blue Gene / Q平台上,针对三个通讯繁重的基准测试(最多可扩展至16,384个进程),RAHTM的通讯时间减少了20%,总体执行时间减少了9%。

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