A massively parallel adaptive finite element method with dynamic load balancing

The authors construct massively parallel adaptive finite element methods for the solution of hyperbolic conservation laws. Spatial discretization is performed by a discontinuous Galerkin finite element method using a basis of piecewise Legendre polynomials. Temporal discretization utilizes a Runge-K...

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Bibliographic Details
Main Authors: Devine, K. D., Flaherty, J. E., Wheat, S. R., Maccabe, A. B.
Format: Conference Proceeding
Language:English
Subjects:
Boundary conditions
Computer science
Computer systems organization > Architectures > Distributed architectures > Grid computing
Computer systems organization > Architectures > Parallel architectures
Computer systems organization > Architectures > Parallel architectures > Multicore architectures
Computer systems organization > Architectures > Serial architectures > Superscalar architectures
Computing methodologies > Modeling and simulation > Simulation types and techniques > Massively parallel and high-performance simulations
Concurrent computing
Finite element methods
Hypercubes
Laboratories
Load management
Mathematics of computing > Mathematical analysis > Functional analysis > Approximation
Mathematics of computing > Mathematical analysis > Numerical analysis > Computations in finite fields
Mathematics of computing > Mathematical analysis > Numerical analysis > Computations on polynomials
Moment methods
Parallel processing
Polynomials
Software and its engineering > Software organization and properties > Software system structures > Distributed systems organizing principles > Grid computing
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Summary:The authors construct massively parallel adaptive finite element methods for the solution of hyperbolic conservation laws. Spatial discretization is performed by a discontinuous Galerkin finite element method using a basis of piecewise Legendre polynomials. Temporal discretization utilizes a Runge-Kutta method. Dissipative fluxes and projection limiting prevent oscillations near solution discontinuities. The resulting method is of high order and may be parallelized efficiently on MIMD computers. The authors demonstrate parallel efficiency through computations on a 1024-processor nCUBE/2 hypercube. They present results using adaptive p-refinement to reduce the computational cost of the method, and tiling, a dynamic, element-based data migration system that maintains global load balance of the adaptive method by overlapping neighborhoods of processors that each perform local balancing.
ISSN:1063-9535
DOI:10.1145/169627.169638