Large-scale graph processing on emerging storage devices

Nima Elyasi, Changho Choi, Anand Sivasubramaniam

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

7 Scopus citations

Abstract

Graph processing is becoming commonplace in many applications to analyze huge datasets. Much of the prior work in this area has assumed I/O devices with considerable latencies, especially for random accesses, using large amount of DRAM to trade-off additional computation for I/O accesses. However, emerging storage devices, including currently popular SSDs, provide fairly comparable sequential and random accesses, making these prior solutions inefficient. In this paper, we point out this inefficiency, and propose a new graph partitioning and processing framework to leverage these new device capabilities. We show experimentally on an actual platform that our proposal can give 2X better performance than a state-of-the-art solution.

Original languageEnglish (US)
Title of host publicationProceedings of the 17th USENIX Conference on File and Storage Technologies, FAST 2019
PublisherUSENIX Association
Pages309-316
Number of pages8
ISBN (Electronic)9781939133090
StatePublished - Jan 1 2019
Event17th USENIX Conference on File and Storage Technologies, FAST 2019 - Boston, United States
Duration: Feb 25 2019Feb 28 2019

Publication series

NameProceedings of the 17th USENIX Conference on File and Storage Technologies, FAST 2019

Conference

Conference17th USENIX Conference on File and Storage Technologies, FAST 2019
CountryUnited States
CityBoston
Period2/25/192/28/19

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

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  • Cite this

    Elyasi, N., Choi, C., & Sivasubramaniam, A. (2019). Large-scale graph processing on emerging storage devices. In Proceedings of the 17th USENIX Conference on File and Storage Technologies, FAST 2019 (pp. 309-316). (Proceedings of the 17th USENIX Conference on File and Storage Technologies, FAST 2019). USENIX Association.