Faster PDE-based simulations using robust composite linear solvers

S. Bhowmick, P. Raghavan, L. McInnes, B. Norris

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

Many large-scale scientific simulations require the solution of nonlinear partial differential equations (PDEs). The effective solution of such nonlinear PDEs depends to a large extent on efficient and robust sparse linear system solution. In this paper, we show how fast and reliable sparse linear solvers can be composed from several underlying linear solution methods. We present a combinatorial framework for developing optimal composite solvers using metrics such as the execution times and failure rates of base solution schemes. We demonstrate how such composites can be easily instantiated using advanced software environments. Our experiments indicate that overall simulation time can be reduced through highly reliable linear system solution using composite solvers.

Original languageEnglish (US)
Pages (from-to)373-387
Number of pages15
JournalFuture Generation Computer Systems
Volume20
Issue number3
DOIs
StatePublished - Apr 1 2004

Keywords

  • Composite methods
  • Large-scale PDE-based simulations
  • Multi-algorithms
  • Newton-Krylov methods
  • Sparse linear solution

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

Fingerprint Dive into the research topics of 'Faster PDE-based simulations using robust composite linear solvers'. Together they form a unique fingerprint.

Cite this