Parallel Acceleration of Krylov Solvers by Factorized Approximate Inverse Preconditioners

• Published in: High Performance Computing for Computational Science - VECPAR 2004 pp. 623 - 636
• Main Authors: Bergamaschi, Luca, Martínez, Ángeles
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Approximate Inverse; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Diagonal Preconditioner; Exact sciences and technology; Nonsymmetric Case; Nonsymmetric Linear System; Software; Sparsity Pattern; Conjugate gradient method; High performance; Parallel algorithm; Krylov subspace method; Iterative method; Advection diffusion equation; Distributed computing; Discretization; Message passing; Sparse matrix; Gradient method; Preconditioning; Accelerator
• ISBN: 9783540254249; 3540254242
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11403937_47

This paper describes and tests a parallel implementation of a factorized approximate inverse preconditioner (FSAI) to accelerate iterative linear system solvers. Such a preconditioner reveals an efficient accelerator of both Conjugate gradient and BiCGstab iterative methods in the parallel solution of large linear systems arising from the discretization of the advection-diffusion equation. The resulting message passing code allows the solution of large problems leading to a very cost-effective algorithm for the solution of large and sparse linear systems.