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Parallel Sparse Matrix-Vector Multiplication Using Accelerators

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Computational Science and Its Applications – ICCSA 2016 (ICCSA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9787))

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Abstract

Sparse matrix-vector multiplication (SpMV) is an essential computational kernel for many applications such as scientific computing. Recently, the number of computing systems equipped with NVIDIA’s GPU and Intel’s Xeon Phi coprocessor based on the MIC architecture has been increasing. Therefore, the importance of effective algorithms for SpMV in these systems is increasing. To the best of our knowledge, while previous studies have reported CPU and GPU implementations of SpMV for a cluster and MIC implementations for a single node, implementations of SpMV for the MIC cluster have not yet been reported. In this paper, we implemented and evaluated parallel SpMV on a GPU cluster and a MIC cluster. As shown by the results, the implementation for MIC achieved relatively high performance in some matrices with a single process, but it could not achieve higher performance than other implementations with 64 MPI processes. Therefore, we implemented and evaluated the single SpMV kernel to improve the performance of parallel SpMV.

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Acknowledgments

This research was supported by Core Research for Evolutional Science and Technology (CREST) of Japan Science and Technology Agency (JST).

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Authors and Affiliations

  1. Graduate School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8573, Japan

    Hiroshi Maeda

  2. Center for Computational Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8577, Japan

    Daisuke Takahashi

Authors
  1. Hiroshi Maeda
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  2. Daisuke Takahashi
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Corresponding author

Correspondence to Daisuke Takahashi .

Editor information

Editors and Affiliations

  1. University of Perugia , Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata , Potenza, Italy

    Beniamino Murgante

  3. Covenant University , Ota, Nigeria

    Sanjay Misra

  4. University of Minho , Braga, Portugal

    Ana Maria A.C. Rocha

  5. Polytechnic University , Bari, Italy

    Carmelo M. Torre

  6. Monash University , Clayton, Victoria, Australia

    David Taniar

  7. Kyushu Sangyo University , Fukuoka, Japan

    Bernady O. Apduhan

  8. Saint Petersburg State University , Saint Petersburg, Russia

    Elena Stankova

  9. Beijing University of Posts & Telecommunication , Beijing, China

    Shangguang Wang

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Maeda, H., Takahashi, D. (2016). Parallel Sparse Matrix-Vector Multiplication Using Accelerators. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2016. ICCSA 2016. Lecture Notes in Computer Science(), vol 9787. Springer, Cham. https://doi.org/10.1007/978-3-319-42108-7_1

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  • DOI: https://doi.org/10.1007/978-3-319-42108-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42107-0

  • Online ISBN: 978-3-319-42108-7

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