Faster PDE-based simulations using robust composite linear solvers

  • Authors:

  • S. Bhowmick;P. Raghavan;L. McInnes;B. Norris

  • Affiliations:

  • Department of Computer Science and Engineering, The Pennsylvania State University, 220 Pond Lab, University Park, PA;Department of Computer Science and Engineering, The Pennsylvania State University, 220 Pond Lab, University Park, PA;Mathematics and Computer Sciences Division, Argonne National Laboratory, 9700 South Cass Ave., Argonne, IL;Mathematics and Computer Sciences Division, Argonne National Laboratory, 9700 South Cass Ave., Argonne, IL

  • Venue:
  • Future Generation Computer Systems - Special issue: Selected numerical algorithms
  • Year:
  • 2004

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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.