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SIMD Parallel Sparse Matrix-Vector and Transposed-Matrix-Vector Multiplication in DD Precision

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High Performance Computing for Computational Science – VECPAR 2016 (VECPAR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10150))

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Abstract

We accelerate a double-precision sparse matrix and DD vector multiplication (DD-SpMV) and its transposition and DD vector multiplication (DD-TSpMV) using SIMD AVX2. AVX2 requires changing the memory access pattern to allow four consecutive 64-bit elements to be read at once. In our previous research, DD-SpMV in CRS using AVX2 needed non-continuous memory load, processing for the remainder, and the summation of four elements in the AVX2 register. These factors degrade the performance of DD-SpMV. In this paper, we compare the storage formats of DD-SpMV and DD-TSpMV for AVX2 to eliminate the performance degradation factors in CRS. Our result indicates that BCRS4x1, whose block size fits the AVX2 register’s length, is effective for DD-SpMV and DD-TSpMV.

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Acknowledgments

This work was supported by JSPS KAKENHI Grant Number 25330144. The authors thank the reviewers for their helpful comments.

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Authors and Affiliations

  1. University of Tsukuba, Tsukuba, Japan

    Toshiaki Hishinuma & Hidehiko Hasegawa

  2. Kogakuin University, Tokyo, Japan

    Hidehiko Hasegawa & Teruo Tanaka

Authors
  1. Toshiaki Hishinuma
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  2. Hidehiko Hasegawa
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  3. Teruo Tanaka
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Corresponding author

Correspondence to Toshiaki Hishinuma .

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Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Inês Dutra

  2. University of Porto, Porto, Portugal

    Rui Camacho

  3. University of Porto, Porto, Portugal

    Jorge Barbosa

  4. Lawrence Berkeley National Laboratory, Berkeley, California, USA

    Osni Marques

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Hishinuma, T., Hasegawa, H., Tanaka, T. (2017). SIMD Parallel Sparse Matrix-Vector and Transposed-Matrix-Vector Multiplication in DD Precision. In: Dutra, I., Camacho, R., Barbosa, J., Marques, O. (eds) High Performance Computing for Computational Science – VECPAR 2016. VECPAR 2016. Lecture Notes in Computer Science(), vol 10150. Springer, Cham. https://doi.org/10.1007/978-3-319-61982-8_4

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  • DOI: https://doi.org/10.1007/978-3-319-61982-8_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61981-1

  • Online ISBN: 978-3-319-61982-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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