Simultaneous computational and data load balancing in distributed-memory setting

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buir.contributor.authorÇeliktuğ, Mestan Fırat
buir.contributor.authorKarsavuran, M. Ozan
buir.contributor.authorAcer, Seher
buir.contributor.authorAykanat, Cevdet
buir.contributor.orcidKarsavuran, M. Ozan|0000-0002-0298-3034
buir.contributor.orcidAcer, Seher|0000-0003-3951-3930
buir.contributor.orcidAykanat, Cevdet|0000-0002-4559-1321
dc.citation.epageC424en_US
dc.citation.issueNumber6en_US
dc.citation.spageC399en_US
dc.citation.volumeNumber44en_US
dc.contributor.authorÇeliktuğ, Mestan Fırat
dc.contributor.authorKarsavuran, M. Ozan
dc.contributor.authorAcer, Seher
dc.contributor.authorAykanat, Cevdet
dc.contributor.editorSterck, Hans De
dc.coverage.spatialUnited Statesen_US
dc.date.accessioned2023-02-21T06:10:25Z
dc.date.available2023-02-21T06:10:25Z
dc.date.issued2022
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractSeveral successful partitioning models and methods have been proposed and used for computational load balancing of irregularly sparse applications in a distributed-memory setting. However, the literature lacks partitioning models and methods that encode both computational and data load balancing. In this article, we try to close this gap in the literature by proposing two hypergraph partitioning (HP) models which simultaneously encode computational and data load balancing. Both models utilize a two-constraint formulation, where the first constraint encodes the computational loads and the second constraint encodes the data loads. In the first model, we introduce explicit data vertices for encoding data load and we replicate those data vertices at each recursive bipartitioning (RB) step for encoding data replication. In the second model, we introduce a data weight distribution scheme for encoding data load and we update those weights at each RB step. The nice property of both proposed models is that they do not necessitate developing a new partitioner from scratch. Both models can easily be implemented by invoking any HP tool that supports multiconstraint partitioning as a two-way partitioner at each RB step. The validity of the proposed models are tested on two widely used irregularly sparse applications: parallel mesh simulations and parallel sparse matrix sparse matrix multiplication. Both proposed models achieve significant improvement over a baseline model.en_US
dc.identifier.doi10.1137/22M1485772en_US
dc.identifier.eissn1095-7197
dc.identifier.issn1064-8275
dc.identifier.urihttp://hdl.handle.net/11693/111556
dc.language.isoEnglishen_US
dc.publisherSIAMen_US
dc.relation.isversionofhttps://doi.org/10.1137/22M1485772en_US
dc.source.titleSIAM Journal on Scientific Computingen_US
dc.subjectComputational load balanceen_US
dc.subjectData load balanceen_US
dc.subjectDistributed-memory systemsen_US
dc.subjectHypergraph partitioningen_US
dc.subjectRecursive bipartitioningen_US
dc.subjectMulti-constraint partitioningen_US
dc.subjectGeneral sparse matrixmatrix multiplicationen_US
dc.subjectMesh partitioningen_US
dc.titleSimultaneous computational and data load balancing in distributed-memory settingen_US
dc.typeArticleen_US

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