基于Blue Gene/Q的代数多重网格性能建模:经验教训

Hormozd Gahvari, W. Gropp, K. E. Jordan, M. Schulz, U. Yang
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引用次数: 6

摘要

IBM蓝色基因/Q代表了大规模并行机器进化的一大步。它具有16核计算节点,每个核同时有四个硬件线程,通过一个五维环面网络连接在一起,具有额外的并行性。机器的核心数量达到数十万个,其中最大的红杉(Sequoia)拥有超过150万个核心。在本文中,我们开发了一个在Blue Gene/Q上求解代数多重网格循环的性能模型,以帮助我们理解这种流行的线性求解器在这种架构上面对的大型稀疏线性系统的问题。我们在IBM的Blue Gene/Q上验证了模型,最后讨论了我们的结果的含义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Performance Modeling of Algebraic Multigrid on Blue Gene/Q: Lessons Learned
The IBM Blue Gene/Q represents a large step in the evolution of massively parallel machines. It features 16-core compute nodes, with additional parallelism in the form of four simultaneous hardware threads per core, connected together by a five-dimensional torus network. Machines are being built with core counts in the hundreds of thousands, with the largest, Sequoia, featuring over 1.5 million cores. In this paper, we develop a performance model for the solve cycle of algebraic multigrid on Blue Gene/Q to help us understand the issues this popular linear solver for large, sparse linear systems faces on this architecture. We validate the model on a Blue Gene/Q at IBM, and conclude with a discussion of the implications of our results.
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