Extreme scale breadth-first search on supercomputers

Koji Ueno, Toyotaro Suzumura, Naoya Maruyama, Katsuki Fujisawa, Satoshi Matsuoka

    研究成果: Chapter in Book/Report/Conference proceedingConference contribution

    11 被引用数 (Scopus)

    抄録

    Breadth-First Search(BFS) is one of the most fundamental graph algorithms used as a component of many graph algorithms. Our new method for distributed parallel BFS can compute BFS for one trillion vertices graph within half a second, using large supercomputers such as the K-Computer. By the use of our proposed algorithm, the K-Computer was ranked 1st in Graph500 using all the 82,944 nodes available on June and November 2015 and June 2016 38,621.4 GTEPS. Based on the hybrid-BFS algorithm by Beamer[3], we devise sets of optimizations for scaling to extreme number of nodes, including a new efficient graph data structure and optimization techniques such as vertex reordering and load balancing. Performance evaluation on the K shows our new BFS is 3.19 times faster on 30,720 nodes than the base version using the previously-known best techniques.

    本文言語英語
    ホスト出版物のタイトルProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
    編集者Ronay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ1040-1047
    ページ数8
    ISBN(電子版)9781467390040
    DOI
    出版ステータス出版済み - 1 1 2016
    イベント4th IEEE International Conference on Big Data, Big Data 2016 - Washington, 米国
    継続期間: 12 5 201612 8 2016

    出版物シリーズ

    名前Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

    その他

    その他4th IEEE International Conference on Big Data, Big Data 2016
    国/地域米国
    CityWashington
    Period12/5/1612/8/16

    All Science Journal Classification (ASJC) codes

    • コンピュータ ネットワークおよび通信
    • 情報システム
    • ハードウェアとアーキテクチャ

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