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dc.contributor.advisorAykanat, Cevdet
dc.contributor.authorÜnsal, Başak
dc.date.accessioned2017-01-02T06:24:42Z
dc.date.available2017-01-02T06:24:42Z
dc.date.copyright2016-12
dc.date.issued2016-12
dc.date.submitted2016-12-29
dc.identifier.urihttp://hdl.handle.net/11693/32596
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2016.en_US
dc.descriptionIncludes bibliographical references (leaves 48-50).en_US
dc.description.abstractSparse matrix-matrix multiplication of the form of C = A x B, C = A x A and C = A x AT is a key operation in various domains and is characterized with high complexity and runtime overhead. There exist models for parallelizing this operation in distributed memory architectures such as outer-product (OP), inner-product (IP), row-by-row-product (RRP) and column-by-column-product (CCP). We focus on row-by-row-product due to its convincing performance, row preprocessing overhead and no symbolic multiplication requirement. The paral- lelization via row-by-row-product model can be achieved using bipartite graphs or hypergraphs. For an efficient parallelization, we can consider multiple volume- based metrics to be reduced such as total volume, maximum volume, etc. Existing approaches for RRP model do not encapsulate multiple volume-based metrics. In this thesis, we propose a two-phase approach to reduce multiple volume- based cost metrics. In the first phase, total volume is reduced with a bipartite graph model. In the second phase, we reduce maximum volume while trying to keep the increase in total volume as small as possible. Our experiments show that the proposed approach is effective at reducing multiple volume-based metrics for different forms of SpGEMM operations.en_US
dc.description.statementofresponsibilityby Başak Ünsal.en_US
dc.format.extentix, 50 leaves : charts (some color).en_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectParallel computingen_US
dc.subjectCombinatorial scientific computingen_US
dc.subjectPartitioningen_US
dc.subjectSparse matricesen_US
dc.subjectSparse operationsen_US
dc.subjectSparse matrix matrix multiplicationen_US
dc.titleReducing communication volume overhead in large-scale parallel SpGEMMen_US
dc.title.alternativeBüyük ölçekli paralel SyGEMM'de iletişim hacmini düşürmeen_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB154952
dc.embargo.release2019-12-29


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