NVM-based Hybrid BFS with memory efficient data structure

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

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

    7 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publicationProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
    EditorsWo Chang, Jun Huan, Nick Cercone, Saumyadipta Pyne, Vasant Honavar, Jimmy Lin, Xiaohua Tony Hu, Charu Aggarwal, Bamshad Mobasher, Jian Pei, Raghunath Nambiar
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages529-538
    Number of pages10
    ISBN (Electronic)9781479956654
    DOIs
    Publication statusPublished - Jan 7 2015
    Event2nd IEEE International Conference on Big Data, IEEE Big Data 2014 - Washington, United States
    Duration: Oct 27 2014Oct 30 2014

    Publication series

    NameProceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014

    Other

    Other2nd IEEE International Conference on Big Data, IEEE Big Data 2014
    CountryUnited States
    CityWashington
    Period10/27/1410/30/14

    All Science Journal Classification (ASJC) codes

    • Artificial Intelligence
    • Information Systems

    Fingerprint Dive into the research topics of 'NVM-based Hybrid BFS with memory efficient data structure'. Together they form a unique fingerprint.

  • Cite this

    Iwabuchi, K., Sato, H., Yasui, Y., Fujisawa, K., & Matsuoka, S. (2015). NVM-based Hybrid BFS with memory efficient data structure. In W. Chang, J. Huan, N. Cercone, S. Pyne, V. Honavar, J. Lin, X. T. Hu, C. Aggarwal, B. Mobasher, J. Pei, & R. Nambiar (Eds.), Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014 (pp. 529-538). [7004270] (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2014.7004270