High-Quality Shared-Memory Graph Partitioning

Partitioning graphs into blocks of roughly equal size such that few edges run between blocks is a frequently needed operation in processing graphs. Recently, size, variety, and structural complexity of these networks has grown dramatically. Unfortunately, previous approaches to parallel graph partit...

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Vydané v:	IEEE transactions on parallel and distributed systems Ročník 31; číslo 11; s. 2710 - 2722
Hlavní autori:	Akhremtsev, Yaroslav, Sanders, Peter, Schulz, Christian
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.11.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Balance criterion Clustering algorithms Complex networks Contracts Graphs label propagation local search Parallel algorithms Parallel graph partitioning Partitioning Partitioning algorithms Program processors shared-memory parallelism
ISSN:	1045-9219, 1558-2183
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Abstract	Partitioning graphs into blocks of roughly equal size such that few edges run between blocks is a frequently needed operation in processing graphs. Recently, size, variety, and structural complexity of these networks has grown dramatically. Unfortunately, previous approaches to parallel graph partitioning have problems in this context since they often show a negative trade-off between speed and quality. We present an approach to multi-level shared-memory parallel graph partitioning that produces balanced solutions, shows high speedups for a variety of large graphs and yields very good quality independently of the number of cores used. For example, in an extensive experimental study, at 79 cores, one of our closest competitors is faster but fails to meet the balance criterion in the majority of cases and another is mostly slower and incurs about 13 percent larger cut size. Important ingredients include parallel label propagation for both coarsening and refinement, parallel initial partitioning, a simple yet effective approach to parallel localized local search, and fast locality preserving hash tables.
AbstractList	Partitioning graphs into blocks of roughly equal size such that few edges run between blocks is a frequently needed operation in processing graphs. Recently, size, variety, and structural complexity of these networks has grown dramatically. Unfortunately, previous approaches to parallel graph partitioning have problems in this context since they often show a negative trade-off between speed and quality. We present an approach to multi-level shared-memory parallel graph partitioning that produces balanced solutions, shows high speedups for a variety of large graphs and yields very good quality independently of the number of cores used. For example, in an extensive experimental study, at 79 cores, one of our closest competitors is faster but fails to meet the balance criterion in the majority of cases and another is mostly slower and incurs about 13 percent larger cut size. Important ingredients include parallel label propagation for both coarsening and refinement, parallel initial partitioning, a simple yet effective approach to parallel localized local search, and fast locality preserving hash tables.
Author	Sanders, Peter Schulz, Christian Akhremtsev, Yaroslav
Author_xml	– sequence: 1 givenname: Yaroslav surname: Akhremtsev fullname: Akhremtsev, Yaroslav email: classboxmail@gmail.com organization: Google, Zurich, Switzerland – sequence: 2 givenname: Peter surname: Sanders fullname: Sanders, Peter email: sanders@kit.edu organization: Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany – sequence: 3 givenname: Christian orcidid: 0000-0002-2823-3506 surname: Schulz fullname: Schulz, Christian email: christian.schulz@univie.ac.at organization: Faculty of Computer Science, University of Vienna, Vienna, Austria
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SubjectTerms	Balance criterion Clustering algorithms Complex networks Contracts Graphs label propagation local search Parallel algorithms Parallel graph partitioning Partitioning Partitioning algorithms Program processors shared-memory parallelism
Title	High-Quality Shared-Memory Graph Partitioning
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