Krylov methods preconditioned by Factorized Sparse Approximate Inverses (FSAI) are an efficient approach for the solution of symmetric positive definite linear systems on massively parallel computers. However, FSAI often suffers from a high set-up cost, especially in ill-conditioned problems. In this communication we propose a novel algorithm for the FSAI computation that makes use of the concept of supernode borrowed from sparse LU factorizations and direct methods.

A Novel Factorized Sparse Approximate Inverse Preconditioner with Supernodes

FERRONATO, MASSIMILIANO;JANNA, CARLO;GAMBOLATI, GIUSEPPE
2015

Abstract

Krylov methods preconditioned by Factorized Sparse Approximate Inverses (FSAI) are an efficient approach for the solution of symmetric positive definite linear systems on massively parallel computers. However, FSAI often suffers from a high set-up cost, especially in ill-conditioned problems. In this communication we propose a novel algorithm for the FSAI computation that makes use of the concept of supernode borrowed from sparse LU factorizations and direct methods.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3161055
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