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Benchmarking SpMV Methods on Many-Core Platforms

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Benchmarking, Measuring, and Optimizing (Bench 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11459))

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Abstract

SpMV is an essential kernel existing in many HPC and data center applications. Meanwhile, the emerging many-core hardware provides promising computational power, and is widely used for acceleration. Many methods and formats have been proposed aiming at better performance of SpMV on many-core platforms. However, there is still lack of a comprehensive comparison of SpMV methods to show their performance difference on sparse matrices with various sparse patterns. Moreover, there is still no systematic work to bridge the gap between SpMV performance and sparse pattern.

In this paper, we investigate the performance of 27 SpMV methods with 1500+ sparse matrices on two many-core platforms: Intel Xeon Phi (Knights Landing 7250) and Nvidia GPGPU (Tesla M40). Our work shows that no single SpMV methods is optimal for all sparse patterns, but some methods can achieve approximately the best performance on most sparse matrices. We further select 13 features to describe the sparse pattern and analyze their correlations to the performance of each SpMV method. Our observations should help other researchers and practitioners to better understand the SpMV performance and provide implications to guide the selection of suitable SpMV method.

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Acknowledgement

This work was supported partially by National Key R&D Program of China (2016YFB1000201), and the National Natural Science Foundation of China (Grant No. 61420106013), and Youth Innovation Promotion Association of Chinese Academy of Sciences (2013073).

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Authors and Affiliations

  1. State Key Laboratory of Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Biwei Xie & Yungang Bao

  2. Department of Computer Science, Princeton University, Princeton, USA

    Zhen Jia

  3. University of Chinese Academy of Sciences, Beijing, China

    Yungang Bao

Authors
  1. Biwei Xie
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  2. Zhen Jia
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  3. Yungang Bao
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Corresponding author

Correspondence to Biwei Xie .

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Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Chen Zheng

  2. Chinese Academy of Sciences, Beijing, China

    Jianfeng Zhan

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Xie, B., Jia, Z., Bao, Y. (2019). Benchmarking SpMV Methods on Many-Core Platforms. In: Zheng, C., Zhan, J. (eds) Benchmarking, Measuring, and Optimizing. Bench 2018. Lecture Notes in Computer Science(), vol 11459. Springer, Cham. https://doi.org/10.1007/978-3-030-32813-9_19

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  • DOI: https://doi.org/10.1007/978-3-030-32813-9_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32812-2

  • Online ISBN: 978-3-030-32813-9

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