Partitioning hypergraphs in scientific computing applications through vertex separators on graphs

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buir.contributor.authorAykanat, Cevdet
dc.citation.epageA992en_US
dc.citation.issueNumber2en_US
dc.citation.spageA970en_US
dc.citation.volumeNumber34en_US
dc.contributor.authorKayaaslan, E.en_US
dc.contributor.authorPinar, A.en_US
dc.contributor.authorÇatalyürek, U.en_US
dc.contributor.authorAykanat, Cevdeten_US
dc.date.accessioned2015-07-28T12:04:49Z
dc.date.available2015-07-28T12:04:49Z
dc.date.issued2012en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractThe modeling flexibility provided by hypergraphs has drawn a lot of interest from the combinatorial scientific community, leading to novel models and algorithms, their applications, and development of associated tools. Hypergraphs are now a standard tool in combinatorial scientific computing. The modeling flexibility of hypergraphs, however, comes at a cost: algorithms on hypergraphs are inherently more complicated than those on graphs, which sometimes translates to nontrivial increases in processing times. Neither the modeling flexibility of hypergraphs nor the runtime efficiency of graph algorithms can be overlooked. Therefore, the new research thrust should be how to cleverly trade off between the two. This work addresses one method for this trade-off by solving the hypergraph partitioning problem by finding vertex separators on graphs. Specifically, we investigate how to solve the hypergraph partitioning problem by seeking a vertex separator on its net intersection graph (NIG), where each net of the hypergraph is represented by a vertex, and two vertices share an edge if their nets have a common vertex. We propose a vertex-weighting scheme to attain good node-balanced hypergraphs, since the NIG model cannot preserve node-balancing information. Vertex-removal and vertex-splitting techniques are described to optimize cut-net and connectivity metrics, respectively, under the recursive bipartitioning paradigm. We also developed implementations of our proposed hypergraph partitioning formulations by adopting and modifying a state-of-the-art graph partitioning by vertex separator tool onmetis. Experiments conducted on a large collection of sparse matrices demonstrate the effectiveness of our proposed techniques. (c) 2012 Society for Industrial and Applied Mathematics.en_US
dc.description.provenanceMade available in DSpace on 2015-07-28T12:04:49Z (GMT). No. of bitstreams: 1 10.1137-100810022.pdf: 461748 bytes, checksum: 70a9211671c4af960d7b04c7b86fa238 (MD5)en
dc.identifier.doi10.1137/100810022en_US
dc.identifier.eissn1095-7197
dc.identifier.issn1064-8275
dc.identifier.urihttp://hdl.handle.net/11693/13160
dc.language.isoEnglishen_US
dc.publisherSociety for Industrial and Applied Mathematicsen_US
dc.relation.isversionofhttp://dx.doi.org/10.1137/100810022en_US
dc.source.titleSIAM Journal on Scientific Computingen_US
dc.subjectHypergraph partitioningen_US
dc.subjectCombinatorial scientific computingen_US
dc.subjectGraph partitioning by vertex separatoren_US
dc.subjectSparse matricesen_US
dc.titlePartitioning hypergraphs in scientific computing applications through vertex separators on graphsen_US
dc.typeArticleen_US

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