Abstract. The efficient use of multicore architectures for sparse matrixvector multiplication (SpMV) is currently an open challenge. One algorithm which makes use of SpMV is the ma...
— We consider memory subsystem optimizations for improving the performance of sparse scientific computation while reducing the power consumed by the CPU and memory. We first co...
A generalized or tunable-kernel model is proposed for probability density function estimation based on an orthogonal forward regression procedure. Each stage of the density estimat...
Sparse LU factorization with partial pivoting is important for many scienti c applications and delivering high performance for this problem is di cult on distributed memory machin...
Given a certain function f, various methods have been proposed in the past for addressing the important problem of computing the matrix-vector product f(A)b without explicitly comp...