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CSPR: Column Only SPARSE Matrix Representation for Performance Improvement on GPU Architecture

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Advances in Parallel Distributed Computing (PDCTA 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 203))

Abstract

General purpose computation on graphics processing unit (GPU) is prominent in the high performance computing era of this time. Porting or accelerating the data parallel applications onto GPU gives the default performance improvement because of the increased computational units. Better performances can be seen if application specific fine tuning is done with respect to the architecture under consideration. One such very widely used computation intensive kernel is sparse matrix vector multiplication (SPMV) in sparse matrix based applications. Most of the existing data format representations of sparse matrix are developed with respect to the central processing unit (CPU) or multi cores. This paper gives a new format for sparse matrix representation with respect to graphics processor architecture that can give 2x to 5x performance improvement compared to CSR (compressed row format), 2x to 54x performance improvement with respect to COO (coordinate format) and 3x to 10 x improvement compared to CSR vector format for the class of application that fit for the proposed new format. It also gives 10% to 133% improvements in memory transfer (of only access information of sparse matrix) between CPU and GPU. This paper gives the details of the new format and its requirement with complete experimentation details and results of comparison.

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Author information

Authors and Affiliations

  1. Department of Information Technology, NITK Surathkal, Mangalore, Karnataka, India, 575 025

    B. Neelima & Prakash S. Raghavendra

Authors
  1. B. Neelima
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  2. Prakash S. Raghavendra
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Editor information

Editors and Affiliations

  1. Wireilla Net Solutions PTY Ltd, Melbourne, Victoria, Australia

    Dhinaharan Nagamalai

  2. Institut Telecom/Telecom SudParis, 9, rue Charles Fourier, 91011, Evry Cedex, France

    Eric Renault

  3. Manonmaniam Sundaranar University, Thirunelveli, Tamil Nadu, India

    Murugan Dhanuskodi

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© 2011 Springer-Verlag Berlin Heidelberg

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Neelima, B., Raghavendra, P.S. (2011). CSPR: Column Only SPARSE Matrix Representation for Performance Improvement on GPU Architecture. In: Nagamalai, D., Renault, E., Dhanuskodi, M. (eds) Advances in Parallel Distributed Computing. PDCTA 2011. Communications in Computer and Information Science, vol 203. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24037-9_58

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  • DOI: https://doi.org/10.1007/978-3-642-24037-9_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24036-2

  • Online ISBN: 978-3-642-24037-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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