TY - GEN
T1 - Performance Evaluation of Supercomputer Fugaku using Breadth-First Search Benchmark in Graph500
AU - Nakao, Masahiro
AU - Ueno, Koji
AU - Fujisawa, Katsuki
AU - Kodama, Yuetsu
AU - Sato, Mitsuhisa
N1 - Funding Information:
ACKNOWLEDGEMENTS This work was partially funded by the MEXT program for the Development and Improvement for the Next Generation Ultra High-Speed Computer System, under its Subsidies for Operating the Specific Advanced Large Research Facilities.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/9
Y1 - 2020/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85096215310&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85096215310&partnerID=8YFLogxK
U2 - 10.1109/CLUSTER49012.2020.00053
DO - 10.1109/CLUSTER49012.2020.00053
M3 - Conference contribution
AN - SCOPUS:85096215310
T3 - Proceedings - IEEE International Conference on Cluster Computing, ICCC
SP - 408
EP - 409
BT - Proceedings - 2020 IEEE International Conference on Cluster Computing, CLUSTER 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE International Conference on Cluster Computing, CLUSTER 2020
Y2 - 14 September 2020 through 17 September 2020
ER -