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FlexBFS: a parallelism-aware implementation of breadth-first search on GPU

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Published:25 February 2012Publication History
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Abstract

In this paper, we present FlexBFS, a parallelism-aware implementation for breadth-first search on GPU. Our implementation can adjust the computation resources according to the feedback of available parallelism dynamically. We also optimized our program in three ways: (1)a simplified two-level queue management,(2)a combined kernel strategy and (3)a high-degree vertices specialization approach. Our experimental results show that it can achieve 3~20 times speedup against the fastest serial version, and can outperform the TBB based multi-threading CPU version and the previous most effective GPU version on all types of input graphs.

References

  1. P. Harish and P. J. Narayanan. Accelerating large graph algorithms on the gpu using cuda. In HiPC'07. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. S. Hong, S. K. Kim, T. Oguntebi, and K. Olukotun. Accelerating cuda graph algorithms at maximum warp. In PPoPP'11. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. L. Luo, M. Wong, and W. mei Hwu. An effective gpu implementation of breadth-first search. In 47th DAC 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. S. Xiao and W. chun Feng. Inter-block gpu communication via fast barrier synchronization. In IPDPS 2010.Google ScholarGoogle Scholar

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  1. FlexBFS: a parallelism-aware implementation of breadth-first search on GPU

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              • Published in

                cover image ACM SIGPLAN Notices
                ACM SIGPLAN Notices  Volume 47, Issue 8
                PPOPP '12
                August 2012
                334 pages
                ISSN:0362-1340
                EISSN:1558-1160
                DOI:10.1145/2370036
                Issue’s Table of Contents
                • cover image ACM Conferences
                  PPoPP '12: Proceedings of the 17th ACM SIGPLAN symposium on Principles and Practice of Parallel Programming
                  February 2012
                  352 pages
                  ISBN:9781450311601
                  DOI:10.1145/2145816

                Copyright © 2012 Authors

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                Association for Computing Machinery

                New York, NY, United States

                Publication History

                • Published: 25 February 2012

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