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English(EN) Data-Driven Prediction of Optimal MPI Rank Placement and Node Count for Memory-Bound CFD on AMD EPYC Clusters

新研究优化AMD EPYC集群上的CFD模拟

作者 PulseAugur 编辑部 · [1 个来源] ·

本文提出了一种数据驱动的方法，用于优化内存密集型OpenFOAM模拟在AMD EPYC集群上的放置和节点计数。研究人员分析了五个Zen 4和Zen 5 EPYC处理器上的560种配置，以了解性能和成本之间的权衡。研究发现，对于较小的网格，使用单个节点通常最有效，而较大的网格由于超线性加速而受益于增加节点数。对于中等大小的网格，速度和成本之间出现了真正的权衡，其中实现最大速度可能会带来显著更高的成本。 AI

排序理由 研究论文，详细介绍了特定硬件和软件的性能优化。[lever_c_demoted from research: ic=1 ai=0.1]

在 arXiv cs.LG 阅读 →

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新研究优化AMD EPYC集群上的CFD模拟

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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Marcin Lawenda, {\L}ukasz Szustak, Aleksandra Krasicka ·

    面向AMD EPYC集群上内存密集型CFD的基于数据的最优MPI Rank放置和节点计数预测

    arXiv:2610.23383v1 Announce Type: cross Abstract: Users who run a memory-bound OpenFOAM case on an AMD EPYC cluster must decide how many nodes to use and how many MPI ranks to place on each core complex. The fastest choice is often not the cheapest one. This paper revisits our ea…

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