Adaptive sparse matrix representation for efficient matrix-vector multiplication
P. Zardoshti1,2; F. Khunjush1,2; H. Sarbazi-Azad2,3 1 Shiraz University, Shiraz, Iran2 Institute for Research in Fundamental Sciences (IPM), Tehran, Iran3 Sharif University of Technology, Tehran, Iran
Abstract
Sparse matrix-vector multiplication (SpMV) is a fundamental computational kernel used in scientific and engineering applications. The nonzero elements of sparse matrices are represented in different formats, and a single sparse matrix representation is not suitable for all sparse matrices with different sparsity patterns. Extensive studies have been done on improving the performance of sparse matrices processing on different platforms. Graphics ...
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