Efficient sparse matrix-vector multiplication on cache-based GPUs - Reguly István Zoltán; Giles M (Innovative Parallel Computing (InPar), 2012, 2012)
Sparse matrix-vector multiplication is an integral part of many scientific algorithms. Several studies have shown that it is a bandwidth-limited operation on current hardware. On cache-based architectures the main factors that influence performance are spatial locality in accessing the matrix, and temporal locality in re-using the elements of the vector. © 2012 IEEE.