Parallel multilevel sparse approximate inverse preconditioners in large sparse matrix computations

Kai Wang, Jun Zhang, Chi Shen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

We investigate the use of the multistep successive preconditioning strategies (MSP) to construct a class of parallel multilevel sparse approximate inverse (SAI) preconditioners. We do not use independent set ordering, but a diagonal dominance based matrix permutation to build a multilevel structure. The purpose of introducing multilevel structure into SAI is to enhance the robustness of SAI for solving difficult problems. Forward and backward preconditioning iteration and two Schur complement preconditioning strategies are proposed to improve the performance and to reduce the storage cost of the multilevel preconditioners. One version of the parallel multilevel SAI preconditioner based on the MSP strategy is implemented. Numerical experiments for solving a few sparse matrices on a distributed memory parallel computer are reported.

Original languageEnglish
Title of host publicationProceedings of the 2003 ACM/IEEE Conference on Supercomputing, SC 2003
DOIs
StatePublished - 2003
Event2003 ACM/IEEE Conference on Supercomputing, SC 2003 - Phoenix, AZ, United States
Duration: Nov 15 2003Nov 21 2003

Publication series

NameProceedings of the 2003 ACM/IEEE Conference on Supercomputing, SC 2003

Conference

Conference2003 ACM/IEEE Conference on Supercomputing, SC 2003
Country/TerritoryUnited States
CityPhoenix, AZ
Period11/15/0311/21/03

Keywords

  • Parallel preconditioning
  • multilevel pre-conditioning
  • sparse approximate inverse

ASJC Scopus subject areas

  • Software

Fingerprint

Dive into the research topics of 'Parallel multilevel sparse approximate inverse preconditioners in large sparse matrix computations'. Together they form a unique fingerprint.

Cite this