Performance Evaluation of Supercomputer Fugaku using Breadth-First Search Benchmark in Graph500

Masahiro Nakao, Koji Ueno, Katsuki Fujisawa, Yuetsu Kodama, Mitsuhisa Sato

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

There is increasing demand for the high-speed processing of large-scale graphs in various fields. However, such graph processing requires irregular calculations, making it difficult to scale performance on large-scale distributed memory systems. Against this background, Graph500, a competition for evaluating the performance of large-scale graph processing, has been held. We developed breadth-first search (BFS), which is one of the benchmark kernels used in Graph500, and took the top spot a total of 10 times using the K computer. In this paper, we tune BFS performance and evaluate it using the supercomputer Fugaku, which is the successor to the K computer. The results of evaluating BFS for a large-scale graph composed of about 1.1 trillion vertices and 17.6 trillion edges using 92,160 nodes of Fugaku indicate that Fugaku has 2.27 times the performance of the K computer. Fugaku took the top spot on Graph500 in June 2020.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE International Conference on Cluster Computing, CLUSTER 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages408-409
Number of pages2
ISBN (Electronic)9781728166773
DOIs
Publication statusPublished - Sep 2020
Event22nd IEEE International Conference on Cluster Computing, CLUSTER 2020 - Kobe, Japan
Duration: Sep 14 2020Sep 17 2020

Publication series

NameProceedings - IEEE International Conference on Cluster Computing, ICCC
Volume2020-September
ISSN (Print)1552-5244

Conference

Conference22nd IEEE International Conference on Cluster Computing, CLUSTER 2020
CountryJapan
CityKobe
Period9/14/209/17/20

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Signal Processing

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