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Segmented Merge: A New Primitive for Parallel Sparse Matrix Computations

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Network and Parallel Computing (NPC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12639))

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Abstract

Segmented operations, such as segmented sum, segmented scan and segmented sort, are important building blocks for parallel irregular algorithms. We in this work propose a new parallel primitive called segmented merge. Its function is in parallel merging q sub-segments to p segments, both of nonuniform lengths. We implement the segmented merge primitive on GPUs and demonstrate its efficiency on parallel sparse matrix transposition (SpTRANS) and sparse matrix-matrix multiplication (SpGEMM) operations.

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Notes

  1. 1.

    We in this paper call “sub-array” “segment”, since each segment further includes at least one “sub-segment”. In this way, we can avoid using terms like “sub-array” and “sub-sub-array”.

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Acknowledgments

We would like to thank the invaluable comments from all the reviewers. Weifeng Liu is the corresponding author of this paper. This research was supported by the Science Challenge Project under Grant No. TZZT2016002, the National Natural Science Foundation of China under Grant No. 61972415, and the Science Foundation of China University of Petroleum, Beijing under Grant No. 2462019YJRC004, 2462020XKJS03.

Author information

Authors and Affiliations

  1. Super Scientific Software Laboratory, Department of Computer Science and Technology, China University of Petroleum-Beijing, Beijing, China

    Haonan Ji, Shibo Lu & Weifeng Liu

  2. Department of Computer Science, Virginia Tech, Blacksburg, USA

    Kaixi Hou

  3. Department of Computer Science and Engineering, The Ohio State University, Columbus, USA

    Hao Wang

  4. Faculty of Technical Sciences, Aarhus University, Aarhus, Denmark

    Brian Vinter

Authors
  1. Haonan Ji
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  2. Shibo Lu
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  3. Kaixi Hou
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  4. Hao Wang
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  5. Weifeng Liu
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  6. Brian Vinter
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Corresponding author

Correspondence to Weifeng Liu .

Editor information

Editors and Affiliations

  1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Xin He

  2. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    En Shao

  3. Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

    Guangming Tan

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Ji, H., Lu, S., Hou, K., Wang, H., Liu, W., Vinter, B. (2021). Segmented Merge: A New Primitive for Parallel Sparse Matrix Computations. In: He, X., Shao, E., Tan, G. (eds) Network and Parallel Computing. NPC 2020. Lecture Notes in Computer Science(), vol 12639. Springer, Cham. https://doi.org/10.1007/978-3-030-79478-1_15

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  • DOI: https://doi.org/10.1007/978-3-030-79478-1_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-79477-4

  • Online ISBN: 978-3-030-79478-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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