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Optimal low-latency network topologies for cluster performance enhancement

  • Yuefan Deng
  • , Meng Guo
  • , Alexandre F. Ramos
  • , Xiaolong Huang
  • , Zhipeng Xu
  • , Weifeng Liu
  • Qilu University of Technology
  • Universidade de São Paulo
  • Stony Brook University
  • Sun Yat-Sen University
  • University of Jinan

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

We propose that clusters interconnected with network topologies having minimal mean path length will increase their processing speeds. We approach our heuristic by constructing clusters of up to 32 nodes having torus, ring, Chvatal, Wagner, Bidiakis and optimal topology for minimal mean path length and by simulating the performance of 256 nodes clusters with the same network topologies. The optimal (or near-optimal) low-latency network topologies are found by minimizing the mean path length of regular graphs. The selected topologies are benchmarked using ping-pong messaging, the MPI collective communications and the standard parallel applications including effective bandwidth, FFTE, Graph 500 and NAS parallel benchmarks. We established strong correlations between the clusters’ performances and the network topologies, especially the mean path lengths, for a wide range of applications. In communication-intensive benchmarks, optimal graphs enabled network topologies with multifold performance enhancement in comparison with mainstream graphs. It is striking that mere adjustment of the network topology suffices to reclaim performance from the same computing hardware.

Original languageEnglish
Pages (from-to)9558-9584
Number of pages27
JournalJournal of Supercomputing
Volume76
Issue number12
DOIs
StatePublished - Dec 1 2020

Keywords

  • Benchmarks
  • Graph theory
  • Latency
  • Network topology

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