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Parallel algorithms for masked sparse matrix-matrix products

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Aydin BuluçAuthors Info & Claims

PPoPP '22: Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

Pages 453 - 454

https://doi.org/10.1145/3503221.3508430

Published: 28 March 2022 Publication History

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Abstract

Computing the product of two sparse matrices (SpGEMM) is a fundamental operation in various combinatorial and graph algorithms as well as various bioinformatics and data analytics applications for computing inner-product similarities. For an important class of algorithms, only a subset of the output entries are needed, and the resulting operation is known as Masked SpGEMM since a subset of the output entries is considered to be "masked out". In this work, we investigate various novel algorithms and data structures for this rather challenging and important computation, and provide guidelines on how to design a fast Masked-SpGEMM for shared-memory architectures.

References

[1]

Aydin Buluc and John R Gilbert. On the representation and multiplication of hypersparse matrices. In IEEE International Symposium on Parallel and Distributed Processing, pages 1--11. IEEE, 2008.

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[2]

Timothy A Davis. Algorithm 10xx: Suitesparse:graphblas: parallel graph algorithms in the language of sparse linear algebra. draft manuscript.

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Mehmet Deveci, Christian Trott, and Sivasankaran Rajamanickam. Multithreaded sparse matrix-matrix multiplication for many-core and gpu architectures. Parallel Computing, 78:33--46, 2018.

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[4]

Srdan Milakovic, Oguz Selvitopi, Israt Nisa, Zoran Budimlic, and Aydin Buluç. Parallel algorithms for masked sparse matrix-matrix products. CoRR, abs/2111.09947, 2021.

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[5]

Yusuke Nagasaka, Satoshi Matsuoka, Ariful Azad, and Aydin Buluç. Performance optimization, modeling and analysis of sparse matrix-matrix products on multi-core and many-core processors. Parallel Computing, 90:102545, 2019.

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Xu LJia HZhang YWang LJiang XMencagli GDazzi PLowenthal DBadia R(2024)HAM-SpMSpV: an Optimized Parallel Algorithm for Masked Sparse Matrix-Sparse Vector Multiplications on multi-core CPUsProceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing10.1145/3625549.3658680(160-173)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3625549.3658680
Siracusa MSoria-Pardos VSgherzi FRandall JJoseph DMoretó Planas MArmejach A(2023)A Tensor Marshaling Unit for Sparse Tensor Algebra on General-Purpose ProcessorsProceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture10.1145/3613424.3614284(1332-1346)Online publication date: 28-Oct-2023
https://dl.acm.org/doi/10.1145/3613424.3614284
Davis T(2023)Algorithm 1037: SuiteSparse:GraphBLAS: Parallel Graph Algorithms in the Language of Sparse Linear AlgebraACM Transactions on Mathematical Software10.1145/357719549:3(1-30)Online publication date: 19-Sep-2023
https://dl.acm.org/doi/10.1145/3577195

Index Terms

Parallel algorithms for masked sparse matrix-matrix products
1. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms
  2. Mathematical analysis
    1. Numerical analysis
      1. Computations on matrices
2. Software and its engineering
  1. Software organization and properties
    1. Extra-functional properties
      1. Software performance

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PPoPP '22: Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming

April 2022

495 pages

ISBN:9781450392044

DOI:10.1145/3503221

General Chair:
Jaejin Lee
Seoul National University
,
Program Chairs:
Kunal Agrawal
Washington University
,
Michael Spear
Lehigh University

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Published: 28 March 2022

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April 2 - 6, 2022

Seoul, Republic of Korea

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Xu LJia HZhang YWang LJiang XMencagli GDazzi PLowenthal DBadia R(2024)HAM-SpMSpV: an Optimized Parallel Algorithm for Masked Sparse Matrix-Sparse Vector Multiplications on multi-core CPUsProceedings of the 33rd International Symposium on High-Performance Parallel and Distributed Computing10.1145/3625549.3658680(160-173)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3625549.3658680
Siracusa MSoria-Pardos VSgherzi FRandall JJoseph DMoretó Planas MArmejach A(2023)A Tensor Marshaling Unit for Sparse Tensor Algebra on General-Purpose ProcessorsProceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture10.1145/3613424.3614284(1332-1346)Online publication date: 28-Oct-2023
https://dl.acm.org/doi/10.1145/3613424.3614284
Davis T(2023)Algorithm 1037: SuiteSparse:GraphBLAS: Parallel Graph Algorithms in the Language of Sparse Linear AlgebraACM Transactions on Mathematical Software10.1145/357719549:3(1-30)Online publication date: 19-Sep-2023
https://dl.acm.org/doi/10.1145/3577195

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Block ILU Preconditioners for a Nonsymmetric Block-Tridiagonal M-Matrix

A partial pivoting strategy for sparse symmetric matrix decomposition

"Wide or tall" and "sparse matrix dense matrix" multiplications

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