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dc.contributor.advisorAykanat, Cevdet
dc.contributor.authorTorun, Fahreddin Şükrü
dc.date.accessioned2016-01-08T18:24:24Z
dc.date.available2016-01-08T18:24:24Z
dc.date.issued2011
dc.identifier.urihttp://hdl.handle.net/11693/15774
dc.descriptionAnkara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2011.en_US
dc.descriptionThesis (Master's) -- Bilkent University, 2011.en_US
dc.descriptionIncludes bibliographical references leaves 55-60.en_US
dc.description.abstractSparse matrix vector multiplication (SpMxV) of the form y = Ax is a kernel operation in iterative linear solvers used in scientific applications. In these solvers, the SpMxV operation is performed repeatedly with the same sparse matrix through iterations until convergence. Depending on the matrix and its decomposition, parallel SpMxV operation necessitates communication among processors in the parallel environment. The communication can be reduced by intelligent decomposition. However, we can further decrease the communication through data replication and redundant computation. The communication occurs due to the transfer of x-vector entries in row-parallel SpMxV computation. The input vector x of the next iteration is computed from the output vector of the current iteration through linear vector operations. Hence, a processor may compute a y-vector entry redundantly, which leads to a x-vector entry in the following iteration, instead of receiving that x-vector entry from another processor. Thus, redundant computation of that y-vector entry may lead to reduction in communication. In this thesis, we devise a directed-graph-based model that correctly captures the computation and communication pattern for above-mentioned iterative solvers. Moreover, we formulate the communication minimization by utilizing redundant computation of y-vector entries as a combinatorial problem on this directed graph model. We propose two heuristics to solve this combinatorial problem. Experimental results indicate that the communication reducing strategy by redundantly computing is promising.en_US
dc.description.statementofresponsibilityTorun, Fahreddin Şükrüen_US
dc.format.extentx, 60 leavesen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSparse matrix vector multiplicationen_US
dc.subjectSparse matrixen_US
dc.subjectParallelen_US
dc.subjectReplicationen_US
dc.subjectIterative solversen_US
dc.subject.lccQA188 .T67 2011en_US
dc.subject.lcshSparse matrices--Data processing.en_US
dc.subject.lcshIterative methods (Mathematics)en_US
dc.subject.lcshMatrices.en_US
dc.titleMinimizing communication through computational redundancy in parallel iterative solversen_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB130535


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