Efficient sparse matrix-vector multiplication on x86-based many-core processors
Xing Liu, Mikhail Smelyanskiy, Edmond Chow, Pradeep Kumar Dubey · 2013
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.