Parallel implementation of an efficient preconditioned linear solver for grid-based applications in chemical physics. III: Improved parallel scalability for sparse matrixvector products

Wenwu Chen, Bill Poirier

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

The linear solve problems arising in chemical physics and many other fields involve large sparse matrices with a certain block structure, for which special block Jacobi preconditioners are found to be very efficient. In two previous papers [W. Chen, B. Poirier, Parallel implementation of efficient preconditioned linear solver for grid-based applications in chemical physics. I. Block Jacobi diagonalization, J. Comput. Phys. 219 (1) (2006) 185197; W. Chen, B. Poirier, Parallel implementation of efficient preconditioned linear solver for grid-based applications in chemical physics. II. QMR linear solver, J. Comput. Phys. 219 (1) (2006) 198209], a parallel implementation was presented. Excellent parallel scalability was observed for preconditioner construction, but not for the matrixvector product itself. In this paper, we introduce a new algorithm with (1) greatly improved parallel scalability and (2) generalization for arbitrary number of nodes and data sizes.

Original languageEnglish
Pages (from-to)779-782
Number of pages4
JournalJournal of Parallel and Distributed Computing
Volume70
Issue number7
DOIs
StatePublished - Jul 2010

    Fingerprint

Keywords

  • Block Jacobi
  • Chemical physics
  • Eigensolver
  • Linear solver
  • Matrixvector product
  • Parallel computing
  • Preconditioning
  • Sparse matrix

Cite this