Parallel Processing for Computational Continuum Dynamics.

Abstract

The numerical solution of many problems in continuum dynamics is seriously limited by the computation rates attainable on computers with serial architecture. Parallel processing machines can achieve much higher rates. However, applying additional processors to a calculation is only part of the solution. This research was undertaken to develop parallel algorithms for explicit and implicit, Lagrangian and Eulerian finite difference schemes for computational continuum dynamics in one spatial dimension. First, the explicit conservation equations in the Lagrangian reference frame were readily reformulated for concurrent processing. Second, an implicit solution was derived for these equations. This is important because it yields unconditional stability. The parallelism is achieved bia a block implicit numerical scheme. Third, a rezoning algorithm was employed with each Lagrangian integration stem to transform the mesh back to the Eulerian reference frame. The algorithmic development path lead to a parallelization of the processing in blocks of the finite difference zones. AWt each step of this research project, the derived numerical methods provided effective algorithms for exploiting the architectural advantages of the HEP H1000 (Heterogeneous Element Processor) computer. The computational timing data shows significant speed-up with the number of processes. (Author)

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Document Details

Document Type
Technical Report
Publication Date
May 10, 1985
Accession Number
ADA158948

Entities

People

  • J. F. Mcgrath

Tags

DTIC Thesaurus Topics

  • Acoustic Impedance
  • Computational Science
  • Computations
  • Computer Programming
  • Computer Programs
  • Computers
  • Difference Equations
  • Differential Equations
  • Dynamics
  • Equations
  • Ideal Gas Law
  • Mathematical Models
  • Operating Systems
  • Parallel Computing
  • Parallel Processing
  • Parallel Processors
  • Simulations

Readers

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