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Semiautomatic Acceleration of Sparse Matrix-Vector Product Using OpenACC

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Parallel Processing and Applied Mathematics

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9574))

Abstract

The aim of this paper is to show that well known SPARSKIT SpMV routines for Ellpack-Itpack and Jagged Diagonal formats can be easily and successfully adapted to a hybrid GPU-accelerated computer environment using OpenACC. We formulate general guidelines for simple steps that should be done to transform source codes with irregular data access into efficient OpenACC programs. We also advise how to improve the performance of such programs by tuning data structures to utilize hardware properties of GPUs. Numerical experiments show that our accelerated versions of SPARSKIT SpMV routines achieve the performance comparable with the performance of the corresponding CUSPARSE routines optimized by NVIDIA.

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Author information

Authors and Affiliations

  1. Institute of Mathematics, Maria Curie–Skłodowska University, Pl. Marii Curie-Skłodowskiej 1, 20-031, Lublin, Poland

    Przemysław Stpiczyński

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  1. Przemysław Stpiczyński
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Correspondence to Przemysław Stpiczyński .

Editor information

Editors and Affiliations

  1. Czestochowa University of Technolog, Czestochowa, Poland

    Roman Wyrzykowski

  2. Department of Computer Science, University of Southern California, Marina Del Rey, California, USA

    Ewa Deelman

  3. Electrical Engineering & Comput. Science, University of Tennessee, Knoxville, Tennessee, USA

    Jack Dongarra

  4. Czestochowa University of Technology, Institute of Computer & Information Sci., Czestochowa, Poland

    Konrad Karczewski

  5. Department of Computer Science, AGH University of Science and Technology, Krakow, Poland

    Jacek Kitowski

  6. Systèmes d’informations, Big Data et Rec, AGH University of Science and Technology, Krakow, Poland

    Kazimierz Wiatr

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Stpiczyński, P. (2016). Semiautomatic Acceleration of Sparse Matrix-Vector Product Using OpenACC. In: Wyrzykowski, R., Deelman, E., Dongarra, J., Karczewski, K., Kitowski, J., Wiatr, K. (eds) Parallel Processing and Applied Mathematics. Lecture Notes in Computer Science(), vol 9574. Springer, Cham. https://doi.org/10.1007/978-3-319-32152-3_14

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  • DOI: https://doi.org/10.1007/978-3-319-32152-3_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32151-6

  • Online ISBN: 978-3-319-32152-3

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