Scaling lattice QCD beyond 100 GPUs

R. Babich, M. Clark, B. Joó, Guochun Shi, R. Brower, S. Gottlieb
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引用次数: 134

Abstract

Over the past five years, graphics processing units (GPUs) have had a transformational effect on numerical lattice quantum chromodynamics (LQCD) calculations in nuclear and particle physics. While GPUs have been applied with great success to the post-Monte Carlo "analysis" phase which accounts for a substantial fraction of the workload in a typical LQCD calculation, the initial Monte Carlo "gauge field generation" phase requires capability-level supercomputing, corresponding to O(100) GPUs or more. Such strong scaling has not been previously achieved. In this contribution, we demonstrate that using a multi-dimensional parallelization strategy and a domain-decomposed preconditioner allows us to scale into this regime. We present results for two popular discretizations of the Dirac operator, Wilson-clover and improved staggered, employing up to 256 GPUs on the Edge cluster at Lawrence Livermore National Laboratory.
扩展晶格QCD超过100个gpu
在过去的五年中,图形处理单元(gpu)对核物理和粒子物理中的数值点阵量子色动力学(LQCD)计算产生了变革性的影响。虽然gpu已经成功地应用于后蒙特卡罗“分析”阶段(在典型的LQCD计算中占工作负载的很大一部分),但初始蒙特卡罗“测量字段生成”阶段需要能力级超级计算,对应于O(100)个gpu或更多。如此强大的规模以前从未实现过。在这篇文章中,我们证明了使用多维并行化策略和域分解预条件允许我们扩展到这个区域。我们展示了两种流行的狄拉克算子离散化的结果,威尔逊三叶树和改进交错,在劳伦斯利弗莫尔国家实验室的边缘集群上使用多达256个gpu。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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