skip to main content
10.1145/2663761.2664192acmconferencesArticle/Chapter ViewAbstractPublication PagesracsConference Proceedingsconference-collections
research-article

GPU-based matrix multiplication methods for social networks analysis

Published:05 October 2014Publication History

ABSTRACT

A matrix multiplication is a building block for social networks analysis. Recently, there have been various methods proposed for GPU-based matrix multiplications. NVIDIA, one of major manufacturers of GPUs, has also proposed various matrix multiplication methods based on GPUs. In this paper, we introduce the methods, and evaluate their performance via extensive experiments using synthetic and real-world datasets. Our results would help practitioners choose the best one for analyzing real-world social networks.

References

  1. D. Kirk and W. Hwu, Programming Massively Parallel Processors, Morgan Kaufmann, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. V. Volkov and J. Demmel, "Benchmarking GPUs to Tune Dense Linear Algebra," In Proc. of Int'l Conf. on Supercomputing, SC, pp. 1--11, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. G. He et al., "Parallel SimRank Computation on Large Graphs with Iterative Aggregation," In Proc. ACM Int'l Conf. on Knowledge discovery and data mining, ACM SIGKDD, pp. 543--552, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. D. Bae, S. Hwang, and S. Kim, "Constructing Seminal Paper Genealogy," In Proc. ACM Int'l Conf. on Information and knowledge management, ACM CIKM, pp. 2101--2104, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Koren et al., "Matrix factorization techniques for recommender systems," Computer, Vol. 42, No. 8, pp. 30--37, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. NVIDIA CUPARSE and CUBLAS libraries, https://developer.nvidia.com/cuda-toolkitGoogle ScholarGoogle Scholar
  7. csrgemm library, http://on-demand.gputechconf.com/gtc/2012/presentations/S0285-GTC2012-Sparse-Matrix-Multiplication.pdfGoogle ScholarGoogle Scholar
  8. X. Yang, S. Parthasarathy, and P. Sadayappan, "Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining," VLDB Endowment, Vol. 4, No. 4, pp. 231--242, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. S. Ryoo et al., "Optimization Principles and Application Performance Evaluation of a Multithreaded GPU using CUDA," In Proc. ACM Int'l Symp. on Principles and practice of parallel programming, ACM SIGPLAN, pp. 73--82, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. N. Bell and M. Garland, Efficient Sparse Matrix-Vector Multiplication on CUDA, NVIDIA Technical Report, NVIDIA Corporation, 2008.Google ScholarGoogle Scholar
  11. Geforce GT 440 specification, http://www.geforce.com/hardware/desktop-gpus/geforce-gt-440-channelGoogle ScholarGoogle Scholar
  12. Tesla specification, http://www.nvidia.co.kr/content/PDF/kepler/Tesla-K20-Active-BD-06499-001-v04.pdfGoogle ScholarGoogle Scholar
  13. Stanford Large Network Dataset Collection, http://snap.stanford.edu/data/Google ScholarGoogle Scholar
  14. IMC 2007 Data Sets, http://socialnetworks.mpi-sws.org/data-imc2007.htmlGoogle ScholarGoogle Scholar

Index Terms

  1. GPU-based matrix multiplication methods for social networks analysis

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Conferences
      RACS '14: Proceedings of the 2014 Conference on Research in Adaptive and Convergent Systems
      October 2014
      386 pages
      ISBN:9781450330602
      DOI:10.1145/2663761

      Copyright © 2014 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 5 October 2014

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      RACS '14 Paper Acceptance Rate59of251submissions,24%Overall Acceptance Rate393of1,581submissions,25%
    • Article Metrics

      • Downloads (Last 12 months)4
      • Downloads (Last 6 weeks)0

      Other Metrics

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader