Preconditioned Krylov Solvers and Methods for Runtime Loop Parallelization

Abstract

We make a detailed examination of the performance achieved by a Krylov space sparse linear system solver that uses incompletely factored matrices for preconditioners. We compared two related mechanisms for parallelizing the computationally critical sparse triangular solves and sparse numeric incomplete factorizations on a range of test problems. From these comparisons we drew several interesting conclusions about methods that can be used to parallelize loops of the type found here. The performance we obtain is brought into perspective by comparison with timing results from a Cray X/MP supercomputer. Performance on an Encore Multimax/320 with relatively modest computational capabilities comes within a small factor of the performance on a comparable code run on a Cray X/MP.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1988
Accession Number
ADA206388

Entities

People

  • Doug Baxter
  • Joel Salz
  • Martin Schultz
  • Stan Eisenstat

Organizations

  • Yale University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Boundaries
  • Compilers
  • Computations
  • Computer Programs
  • Computer Science
  • Efficiency
  • Elimination
  • Equations
  • Floating Point Operations
  • Linear Systems
  • Lists (Data Structures)
  • Multiprocessors
  • Scheduling (Production)
  • Simulations
  • Sparse Matrix
  • Wavefronts

Fields of Study

  • Computer science

Readers

  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Parallel and Distributed Computing.

Technology Areas

  • Space