Parallel Adaptive Mesh Refinement and Redistribution on Distributed Memory Computers.

Abstract

A procedure to support parallel refinement and redistribution of two dimensional unstructured finite element meshes on distributed memory computers is presented. The procedure uses the mesh topological entity hierarchy as the underlying data structures to easily support the required adjacency information. Mesh refinement is done by employing links back to the geometric representation to place new nodes on the boundary of the domain directly on the curved geometry. The refined mesh is then redistributed by an iterative heuristic based on the Leiss/Reddy 9 load balancing criteria. A fast parallel tree edge-coloring algorithm is used to pair processors having adjacent partitions and forming a tree structure as a result of Leiss/Reddy load request criteria. Excess elements are iteratively migrated from heavily loaded to less loaded processors until load balancing is achieved. The system is implemented on a massively parallel MasPar MP-1 system with a SIMD style of computation and uses message passing primitives to migrate elements during the mesh redistribution phase. Performance results of the redistribution heuristics on various test meshes are given.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1993
Accession Number
ADA290451

Entities

People

  • C. Ozturan
  • H. L. Decougny
  • J. E. Flaherty
  • M. S. Shephard

Organizations

  • Rensselaer Polytechnic Institute

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Boundaries
  • Classification
  • Computational Fluid Dynamics
  • Computational Science
  • Computations
  • Computer Graphics
  • Computer Science
  • Computers
  • Differential Equations
  • Dynamic Loads
  • Equations
  • Finite Element Analysis
  • Geometry
  • Load Distribution
  • New York
  • Partial Differential Equations

Readers

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