Hypergraph partitioning based models and methods for exploiting cache locality in sparse matrix-vector multiplication

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buir.contributor.authorAykanat, Cevdet
dc.citation.epageC262en_US
dc.citation.issueNumber3en_US
dc.citation.spageC237en_US
dc.citation.volumeNumber35en_US
dc.contributor.authorAkbudak, K.en_US
dc.contributor.authorKayaaslan, E.en_US
dc.contributor.authorAykanat, Cevdeten_US
dc.date.accessioned2015-07-28T11:57:30Z
dc.date.available2015-07-28T11:57:30Z
dc.date.issued2013-02-27en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.description.abstractSparse matrix-vector multiplication (SpMxV) is a kernel operation widely used in iterative linear solvers. The same sparse matrix is multiplied by a dense vector repeatedly in these solvers. Matrices with irregular sparsity patterns make it difficult to utilize cache locality effectively in SpMxV computations. In this work, we investigate single-and multiple-SpMxV frameworks for exploiting cache locality in SpMxV computations. For the single-SpMxV framework, we propose two cache-size-aware row/column reordering methods based on one-dimensional (1D) and two-dimensional (2D) top-down sparse matrix partitioning. We utilize the column-net hypergraph model for the 1D method and enhance the row-column-net hypergraph model for the 2D method. The primary aim in both of the proposed methods is to maximize the exploitation of temporal locality in accessing input vector entries. The multiple-SpMxV framework depends on splitting a given matrix into a sum of multiple nonzero-disjoint matrices. We propose a cache-size-aware splitting method based on 2D top-down sparse matrix partitioning by utilizing the row-column-net hypergraph model. The aim in this proposed method is to maximize the exploitation of temporal locality in accessing both input-and output-vector entries. We evaluate the validity of our models and methods on a wide range of sparse matrices using both cache-miss simulations and actual runs by using OSKI. Experimental results show that proposed methods and models outperform state-of-the-art schemes. (c)2013 Society for Industrial and Applied Mathematicsen_US
dc.identifier.doi10.1137/100813956en_US
dc.identifier.eissn1095-7197
dc.identifier.issn1064-8275
dc.identifier.urihttp://hdl.handle.net/11693/11365
dc.language.isoEnglishen_US
dc.publisherSociety for Industrial and Applied Mathematicsen_US
dc.relation.isversionofhttp://dx.doi.org/10.1137/100813956en_US
dc.source.titleSIAM Journal on Scientific Computingen_US
dc.subjectCache Localityen_US
dc.subjectSparse Matrixen_US
dc.subjectMatrix-vector Multiplicationen_US
dc.subjectMatrix Reordering, Computational Hypergraph Modelen_US
dc.subjectHypergraph Partitioningen_US
dc.subjectTraveling Salesman Problemen_US
dc.titleHypergraph partitioning based models and methods for exploiting cache locality in sparse matrix-vector multiplicationen_US
dc.typeArticleen_US
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