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Gunrock: a high-performance graph processing library on the GPU

Published:24 January 2015Publication History
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Abstract

For large-scale graph analytics on the GPU, the irregularity of data access and control flow and the complexity of programming GPUs have been two significant challenges for developing a programmable high-performance graph library. "Gunrock", our graph-processing system, uses a high-level bulk-synchronous abstraction with traversal and computation steps, designed specifically for the GPU. Gunrock couples high performance with a high-level programming model that allows programmers to quickly develop new graph primitives with less than 300 lines of code. We evaluate Gunrock on five graph primitives and show that Gunrock has at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives, and better performance than any other GPU high-level graph library.

References

  1. Z. Fu, M. Personick, and B. Thompson. MapGraph: A high level API for fast development of high performance graph analytics on GPUs. In Proceedings of Workshop on GRAph Data Management Experiences and Systems, GRADES ’14, pages 2:1–2:6, June 2014. doi: 10.1145/2621934.2621936. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. J. E. Gonzalez, Y. Low, H. Gu, D. Bickson, and C. Guestrin. PowerGraph: Distributed graph-parallel computation on natural graphs. In Proceedings of the 10th USENIX Conference on Operating Systems Design and Implementation, OSDI ’12, pages 17–30. USENIX Association, Oct. 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. J. Shun and G. E. Blelloch. Ligra: a lightweight graph processing framework for shared memory. In Proceedings of the 18th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP ’13, pages 135–146, Feb. 2013. doi: 10.1145/2442516.2442530. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. J. Zhong and B. He. Medusa: Simplified graph processing on GPUs. IEEE Transactions on Parallel and Distributed Systems, 25(6):1543– 1552, June 2014. doi: 10.1109/TPDS.2013.111. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      • Published in

        cover image ACM SIGPLAN Notices
        ACM SIGPLAN Notices  Volume 50, Issue 8
        PPoPP '15
        August 2015
        290 pages
        ISSN:0362-1340
        EISSN:1558-1160
        DOI:10.1145/2858788
        • Editor:
        • Andy Gill
        Issue’s Table of Contents
        • cover image ACM Conferences
          PPoPP 2015: Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
          January 2015
          290 pages
          ISBN:9781450332057
          DOI:10.1145/2688500

        Copyright © 2015 Owner/Author

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 24 January 2015

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