Comparative Analysis of GPU-Based Solver Libraries for a Sparse Linear System of Equations

Abstract

In this paper, a comparison of GPU-based linear solver libraries for the solution of sparse positive-definite matrices is presented. These large sparse matrices arise in a number of computational disciplines seeking a solution for partial differential equations. The solution of these matrices is often a time-consuming process that can be reduced by parallel computing. Since the development of GPU for general-purpose computing, a number of numerical solver libraries have evolved that can accelerate the solution procedure. The performance of three solver libraries has been evaluated in this paper for five different test matrices. These test matrices have been taken from different application domains with different sparsity patterns. Results demonstrate a higher speedup from the iterative solver over the direct solver on GPU and also over a multithreaded CPU implementation.