Combinatorial BLAS 2.0: Scaling Combinatorial Algorithms on Distributed-Memory Systems

Combinatorial algorithms such as those that arise in graph analysis, modeling of discrete systems, bioinformatics, and chemistry, are often hard to parallelize. The Combinatorial BLAS library implements key computational primitives for rapid development of combinatorial algorithms in distributed-mem...

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Bibliographic Details
Published in:IEEE transactions on parallel and distributed systems Vol. 33; no. 4; pp. 989 - 1001
Main Authors: Azad, Ariful, Selvitopi, Oguz, Hussain, Md Taufique, Gilbert, John R., Buluc, Aydn
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:1045-9219, 1558-2183
Online Access:Get full text
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Summary:Combinatorial algorithms such as those that arise in graph analysis, modeling of discrete systems, bioinformatics, and chemistry, are often hard to parallelize. The Combinatorial BLAS library implements key computational primitives for rapid development of combinatorial algorithms in distributed-memory systems. During the decade since its first introduction, the Combinatorial BLAS library has evolved and expanded significantly. This article details many of the key technical features of Combinatorial BLAS version 2.0, such as communication avoidance, hierarchical parallelism via in-node multithreading, accelerator support via GPU kernels, generalized semiring support, implementations of key data structures and functions, and scalable distributed I/O operations for human-readable files. Our article also presents several rules of thumb for choosing the right data structures and functions in Combinatorial BLAS 2.0, under various common application scenarios.
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ISSN:1045-9219
1558-2183
DOI:10.1109/TPDS.2021.3094091