Parallel algorithms for finding connected components using linear algebra

Finding connected components is one of the most widely used operations on a graph. Optimal serial algorithms for the problem have been known for half a century, and many competing parallel algorithms have been proposed over the last several decades under various different models of parallel computat...

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Veröffentlicht in:Journal of parallel and distributed computing Jg. 144; H. C; S. 14 - 27
Hauptverfasser: Zhang, Yongzhe, Azad, Ariful, Buluç, Aydın
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Elsevier Inc 01.10.2020
Elsevier
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ISSN:0743-7315, 1096-0848
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Abstract Finding connected components is one of the most widely used operations on a graph. Optimal serial algorithms for the problem have been known for half a century, and many competing parallel algorithms have been proposed over the last several decades under various different models of parallel computation. This paper presents a class of parallel connected-component algorithms designed using linear-algebraic primitives. These algorithms are based on a PRAM algorithm by Shiloach and Vishkin and can be designed using standard GraphBLAS operations. We demonstrate two algorithms of this class, one named LACC for Linear Algebraic Connected Components, and the other named FastSV which can be regarded as LACC’s simplification. With the support of the highly-scalable Combinatorial BLAS library, LACC and FastSV outperform the previous state-of-the-art algorithm by a factor of up to 12x for small to medium scale graphs. For large graphs with more than 50B edges, LACC and FastSV scale to 4K nodes (262K cores) of a Cray XC40 supercomputer and outperform previous algorithms by a significant margin. This remarkable performance is accomplished by (1) exploiting sparsity that was not present in the original PRAM algorithm formulation, (2) using high-performance primitives of Combinatorial BLAS, and (3) identifying hot spots and optimizing them away by exploiting algorithmic insights.
AbstractList Finding connected components is one of the most widely used operations on a graph. Optimal serial algorithms for the problem have been known for half a century, and many competing parallel algorithms have been proposed over the last several decades under various different models of parallel computation. This paper presents a class of parallel connected-component algorithms designed using linear-algebraic primitives. These algorithms are based on a PRAM algorithm by Shiloach and Vishkin and can be designed using standard GraphBLAS operations. We demonstrate two algorithms of this class, one named LACC for Linear Algebraic Connected Components, and the other named FastSV which can be regarded as LACC’s simplification. With the support of the highly-scalable Combinatorial BLAS library, LACC and FastSV outperform the previous state-of-the-art algorithm by a factor of up to 12x for small to medium scale graphs. For large graphs with more than 50B edges, LACC and FastSV scale to 4K nodes (262K cores) of a Cray XC40 supercomputer and outperform previous algorithms by a significant margin. This remarkable performance is accomplished by (1) exploiting sparsity that was not present in the original PRAM algorithm formulation, (2) using high-performance primitives of Combinatorial BLAS, and (3) identifying hot spots and optimizing them away by exploiting algorithmic insights.
Author Azad, Ariful
Buluç, Aydın
Zhang, Yongzhe
Author_xml – sequence: 1
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  surname: Zhang
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  surname: Buluç
  fullname: Buluç, Aydın
  email: abuluc@lbl.gov
  organization: Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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Snippet Finding connected components is one of the most widely used operations on a graph. Optimal serial algorithms for the problem have been known for half a...
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StartPage 14
SubjectTerms Connected Component
Distributed memory
GraphBLAS
Title Parallel algorithms for finding connected components using linear algebra
URI https://dx.doi.org/10.1016/j.jpdc.2020.04.009
https://www.osti.gov/biblio/1632022
Volume 144
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