A comparative study on dynamic and static sparsity patterns in parallel sparse approximate inverse preconditioning

Kai Wang, Sangbae Kim, Jun Zhang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Sparse approximate inverse (SAI) techniques have recently emerged as a new class of parallel preconditioning techniques for solving large sparse linear systems on high performance computers. The choice of the sparsity pattern of the SAI matrix is probably the most important step in constructing an SAI preconditioner. Both dynamic and static sparsity pattern selection approaches have been proposed by researchers. Through a few numerical experiments, we conduct a comparable study on the properties and performance of the SAI preconditioners using the different sparsity patterns for solving some sparse linear systems.

Original languageEnglish
Pages (from-to)203-215
Number of pages13
JournalJournal of Mathematical Modelling and Algorithms
Volume2
Issue number3
DOIs
StatePublished - 2003

Bibliographical note

Funding Information:
★ This research was supported in part by the U.S. National Science Foundation under grants CCR-9902022, CCR-9988165, CCR-0092532, and ACI-0202934, in part by the Japan Research Organization for Information Science & Technology, and in part by the University of Kentucky Research Committee.

Keywords

  • Dynamic and static sparsity pattern
  • Parallel preconditioning
  • Sparse approximate inverse
  • Sparse matrices

ASJC Scopus subject areas

  • Modeling and Simulation
  • Applied Mathematics

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