A Data-Parallel ILUPACK for Sparse General and Symmetric Indefinite Linear Systems

Abstract

The solution of sparse linear systems of large dimension is a critical step in problems that span a diverse range of applications. For this reason, a number of iterative solvers have been developed, among which ILUPACK integrates an inverse-based multilevel ILU preconditioner with appealing numerical properties. In this paper, we enhance the computational performance of ILUPACK by off-loading the execution of several key computational kernels to a Graphics Processing Unit (GPU). In particular, we target the preconditioned GMRES and BiCG methods for sparse general systems and the preconditioned SQMR method for sparse symmetric indefinite problems in ILUPACK. The evaluation on a NVIDIA Kepler GPU shows a sensible reduction of the execution time, while maintaining the convergence rate and numerical properties of the original ILUPACK solver.