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dc.contributor.advisorAykanat, Cevdeten_US
dc.contributor.authorCevahir, Alien_US
dc.date.accessioned2016-07-01T11:08:17Z
dc.date.available2016-07-01T11:08:17Z
dc.date.issued2006
dc.identifier.urihttp://hdl.handle.net/11693/29894
dc.descriptionCataloged from PDF version of article.en_US
dc.description.abstractWeb search engines use ranking techniques to order Web pages in query results. PageRank is an important technique, which orders Web pages according to the linkage structure of the Web. The efficiency of the PageRank computation is important since the constantly evolving nature of the Web requires this computation to be repeated many times. PageRank computation includes repeated iterative sparse matrix-vector multiplications. Due to the enormous size of the Web matrix to be multiplied, PageRank computations are usually carried out on parallel systems. However, efficiently parallelizing PageRank is not an easy task, because of the irregular sparsity pattern of the Web matrix. Graph and hypergraphpartitioning-based techniques are widely used for efficiently parallelizing matrixvector multiplications. Recently, a hypergraph-partitioning-based decomposition technique for fast parallel computation of PageRank is proposed. This technique aims to minimize the communication overhead of the parallel matrix-vector multiplication. However, the proposed technique has a high prepropocessing time, which makes the technique impractical. In this work, we propose 1D (rowwise and columnwise) and 2D (fine-grain and checkerboard) decomposition models using web-site-based graph and hypergraph-partitioning techniques. Proposed models minimize the communication overhead of the parallel PageRank computations with a reasonable preprocessing time. The models encapsulate not only the matrix-vector multiplication, but the overall iterative algorithm. Conducted experiments show that the proposed models achieve fast PageRank computation with low preprocessing time, compared with those in the literature.en_US
dc.description.statementofresponsibilityCevahir, Alien_US
dc.format.extentxii, 78 leaves, graphicsen_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPageRanken_US
dc.subjectParallel Sparse-Matrix Vector Multiplicationen_US
dc.subjectGraph and Hypergraph Partitioningen_US
dc.subject.lccQA188 .C49 2006en_US
dc.subject.lcshSparse matrices Data processing.en_US
dc.titleWeb-site-based partitioning techniques for efficient parallelization of the PageRank computationen_US
dc.typeThesisen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidBILKUTUPB100118


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