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In this paper we propose the optimization of sparse matrix-vector multiplication (SpMV) with CUDA based on matrix bandwidth/profile reduction techniques. Computational time required to access dense vector is decoupled from SpMV computation. By reducing the matrix profile, the time required to access dense vector is reduced by 17% (for SP) and 24% (for DP). Reduced matrix bandwidth enables column index...
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