Parallel Partitioning Strategies for the Adaptive Solution of Conservation Laws.

Abstract

We describe and examine the performance of adaptive methods for solving hyperbolic systems of conservation laws on massively parallel computers. The differential system is approximated by a discontinuous Galerkin finite element method with a hierarchical Legendre piecewise polynomial basis for the spatial discretization. Fluxes at element boundaries are computed by solving an approximate Riemann problem; a projection limiter is applied to keep the average solution monotone; time discretization is performed by Runge Kutta integration; and a p-refinement-based error estimate is used as an enrichment indicator. Adaptive order (p-) and mesh (h-) refinement algorithms are presented and demonstrated. Using an element-based dynamic load balancing algorithm called tiling and adaptive prefinement, parallel efficiencies of over 60% are achieved on a 1024-processor nCUBE/2 hypercube. We also demonstrate a fast, tree-based parallel partitioning strategy for three-dimensional octree-structured meshes. This method produces partition quality comparable to recursive spectral bisection at a greatly reduced cost.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1994
Accession Number
ADA290458

Entities

People

  • J. E. Flaherty
  • Karen D. Devine
  • Raymond M. Loy
  • Stephen R. Wheat

Organizations

  • Rensselaer Polytechnic Institute

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Aircrafts
  • Applied Mathematics
  • Computational Fluid Dynamics
  • Computational Science
  • Computations
  • Computer Science
  • Computers
  • Differential Equations
  • Dynamic Loads
  • Equations
  • Finite Element Analysis
  • Fluid Dynamics
  • Helicopters
  • Parallel Computing
  • Parallel Processing
  • Three Dimensional
  • Two Dimensional

Readers

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