NVM-based Hybrid BFS with memory efficient data structure

Keita Iwabuchi, Hitoshi Sato, Yuichiro Yasui, Katsuki Fujisawa, Satoshi Matsuoka

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

    7 被引用数 (Scopus)

    抄録

    We introduce a memory efficient implementation for the NVM-based Hybrid BFS algorithm that merges redundant data structures to a single graph data structure, while offloading infrequent accessed graph data on NVMs based on the detailed analysis of access patterns, and demonstrate extremely fast BFS execution for large-scale unstructured graphs whose size exceed the capacity of DRAM on the machine. Experimental results of Kronecker graphs compliant to the Graph500 benchmark on a 2-way INTEL Xeon E5-2690 machine with 256 GB of DRAM show that our proposed implementation can achieve 4.14 GTEPS for a SCALE31 graph problem with 231 vertices and 235 edges, whose size is 4 times larger than the size of graphs that the machine can accommodate only using DRAM with only 14.99 % performance degradation. We also show that the power efficiency of our proposed implementation achieves 11.8 MTEPS/W. Based on the implementation, we have achieved the 3rd and 4th position of the Green Graph500 list (2014 June) in the Big Data category.

    本文言語英語
    ホスト出版物のタイトルProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
    編集者Wo Chang, Jun Huan, Nick Cercone, Saumyadipta Pyne, Vasant Honavar, Jimmy Lin, Xiaohua Tony Hu, Charu Aggarwal, Bamshad Mobasher, Jian Pei, Raghunath Nambiar
    出版社Institute of Electrical and Electronics Engineers Inc.
    ページ529-538
    ページ数10
    ISBN(電子版)9781479956654
    DOI
    出版ステータス出版済み - 1 7 2015
    イベント2nd IEEE International Conference on Big Data, IEEE Big Data 2014 - Washington, 米国
    継続期間: 10 27 201410 30 2014

    出版物シリーズ

    名前Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014

    その他

    その他2nd IEEE International Conference on Big Data, IEEE Big Data 2014
    国/地域米国
    CityWashington
    Period10/27/1410/30/14

    All Science Journal Classification (ASJC) codes

    • 人工知能
    • 情報システム

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