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Sparse matrix-vector multiplication (SpMV) is an important kernel in many scientific applications and is known to be memory bandwidth limited. On modern processors with wide SIMD and large numbers of cores, we identify and address several bottlenecks which may limit performance even before memory bandwidth: (a) low SIMD efficiency due to sparsity, (b) overhead due to irregular memory accesses, and (c) load-imbalance due to non-uniform matrix structures.
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Liu et al. (2013) studied this question.
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