AbuBaker, Nabil2016-09-092016-09-092016-082016-082016-09-06http://hdl.handle.net/11693/32211Cataloged from PDF version of article.Thesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2016.Includes bibliographical references (leaves 50-53).Sparse Matrix-Vector multiplication (SpMV) is a very important kernel operation for many scientific applications. For irregular sparse matrices, the SpMV operation suffers from poor cache performance due to the irregular accesses of the input vector entries. In this work, we propose row and column reordering methods based on Graph partitioning (GP) and Hypergraph partitioning (HP) in order to exploit spatial and temporal localities in accessing input vector entries by clustering rows/columns with a similar sparsity pattern close to each other. The proposed methods exploit spatial and temporal localities separately (using either rows or columns of the matrix in a GP or HP method), simultaneously (using both rows and column) and in a two-phased manner(using either rows or columns in each phase). We evaluate the validity of the proposed models on a 60- core Xeon Phi co-processor for a large set of sparse matrices arising from different applications. The performance results confirm the validity and the effectiveness of the proposed methods and models.xiii, 53 leaves : illustrations, charts.Englishinfo:eu-repo/semantics/openAccessSparse matrix-vector multiplicationGraph modelHypergraph modelSpatiotemporalSpatial localityTemporal localityXeon phiMatrix reorderingParallel SpMVThesisB154023