A Parabolic Load Balancing Method

Abstract

This paper presents a diffusive load balancing method for scalable multicomputers. In contrast to other schemes which are provably correct the method scales to large numbers of processors with no increase in run time. In contrast to other schemes which are scalable the method is provably correct and the paper analyzes the rate of convergence. To control aggregate cpu idle time it can be useful to balance the load to specifiable accuracy. The method achieves arbitrary accuracy by proper consideration of numerical error and stability. This paper presents the method, proves correctness, convergence and scalability, and simulates applications to generic problems in computational fluid dynamics (CFD). The applications reveal some useful properties. The method can preserve adjacency relationships among elements of an adapting computational domain. This makes it useful for partitioning unstructured computational grids in concurrent computations. The method can execute asynchronously to balance a subportion of a domain without affecting the rest of the domain. Theory and experiment show the method is efficient on the scalable multicomputers of the present and coming years. The number of floating point operations required per processor to reduce a point disturbance by 90% is 168 on a system of 512 computers and 105 on a system of 1,000,000 computers. On a typical contemporary multicomputer this requires 82.5 us of wall-clock time.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA442993

Entities

People

  • Alan Heirich
  • Stephen Taylor

Organizations

  • California Institute of Technology

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Accuracy
  • Availability
  • Classification
  • Computational Fluid Dynamics
  • Computations
  • Computers
  • Contracts
  • Contrast
  • Convergence
  • Dynamics
  • Errors
  • Floating Point Operations
  • Fluid Dynamics
  • Information Operations
  • Instructions
  • Monitoring

Fields of Study

  • Computer science
  • Engineering

Readers

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