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GStream: a graph streaming processing method for large-scale graphs on GPUs

Published:24 January 2015Publication History
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Abstract

Fast processing graph algorithms for large-scale graphs becomes increasingly important. Besides, there have been many attempts to process graph applications by exploiting the massive amount of parallelism of GPUs. However, most of the existing methods fail to process large-scale graphs that do not fit in GPU device memory. We propose a fast and scalable parallel processing method GStream that fully exploits the computational power of GPUs for processing large-scale graphs (e.g., billions vertices) very efficiently. It exploits the concept of nested-loop theta-join and multiple asynchronous GPU streams. Extensive experimental results show that GStream consistently and significantly outperforms the state-of-the art method.

References

  1. A. Gharaibeh, L. Beltrao Costa, E. Santos-Neto, and M. Ripeanu. A yoke of oxen and a thousand chickens for heavy lifting graph processing. In PACT, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. W.-S. Han, S. Lee, K. Park, J.-H. Lee, M.-S. Kim, J. Kim, and H. Yu, Turbograph: A fast parallel graph engine handling billion-scale graphs in a single pc. In KDD, 2013. 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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