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dc.contributor.authorMaheshwari, P.
dc.contributor.authorShen, Hong
dc.date.accessioned2020-01-03T04:15:33Z
dc.date.available2020-01-03T04:15:33Z
dc.date.issued1998
dc.identifier.issn01678191
dc.identifier.doi10.1016/S0167-8191(98)00004-0
dc.identifier.urihttp://hdl.handle.net/10072/122191
dc.description.abstractThis paper presents a clustering algorithm that partitions node-labelled and edge-labelled directed acyclic precedence graphs (APG) into clusters such that all the clusters have balanced amount of computation load and there is only one communication path between any pair of clusters. The algorithm initially demonstrates all exploitable parallelism instances in a tree structure, then balances the computation load among the parallelism instances, and finally partitions the parallelism instances into clusters which can be scheduled on a set of processors belonging to an MIMD multiprocessor. The comparison results show that the clusters generated by our algorithm could be scheduled in less completion time than the clusters obtained by using other approaches.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherNorth-Holland
dc.publisher.placeNetherlands
dc.relation.ispartofpagefrom893
dc.relation.ispartofpageto909
dc.relation.ispartofissue5-6
dc.relation.ispartofjournalParallel Computing
dc.relation.ispartofvolume24
dc.subject.fieldofresearchDistributed Computing
dc.subject.fieldofresearchCognitive Sciences
dc.subject.fieldofresearchcode0805
dc.subject.fieldofresearchcode1702
dc.titleAn efficient clustering algorithm for partitioning parallel programs
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorShen, Hong


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