Automatic Selection of Sparse Matrix Representation on GPUs
Naser Sedaghati, Te Mu, Louis-Noël Pouchet, Srinivasan Parthasarathy, Ponnuswamy Sadayappan · 2015
Sparse matrix-vector multiplication (SpMV) is a core kernel in numerous applications, ranging from physics simulation and large-scale solvers to data analytics. Many GPU implementations of SpMV have been proposed, targeting several sparse representations and aiming at maximizing overall performance. No single sparse matrix representation is uniformly superior, and the best performing representation varies for sparse matrices with different sparsity patterns.