Efficient sparse matrix-vector multiplication for geophysical electromagnetic codes on Xeon Phi coprocessors

Abstract

Sparse matrix-vector multiplication (spMV) is a fundamental building block of iterative solvers in many scientific applications. spMV is known to perform poorly in modern processors due to excessive pressure over the memory system, overhead of irregular memory accesses and load imbalance due to non-uniform matrix structures. Achieving higher performance requires taking advantage of the features of the matrix and choosing the right sparse storage format to better exploit the target architecture. In this paper we describe an efficient spMV for geophysical electromagnetic simulations on Intel Xeon Phi coprocessors. The unique features of the matrix resulting from electromagnetic problems make it hard to handle with classical sparse storage formats. We propose a matrix decomposition and a tuned storage format that obtains a 4.13x performance improvement over the optimized CSR spMV kernel on Xeon Phi coprocessors.