
Accession Number : ADA295790
Title : Massively Parallel Iterative Methods: Multiscale Preconditioners and Implicit Methods.
Descriptive Note : Final rept. 17 Jun 9130 Sep 94,
Corporate Author : CALIFORNIA UNIV LOS ANGELES DEPT OF MATHEMATICS
Personal Author(s) : Chan, Tony F.
PDF Url : ADA295790
Report Date : 22 MAR 1995
Pagination or Media Count : 8
Abstract : Nonlinear and linear systems of equations often arise in scientific computation, for example in implicit methods in Computational Fluid Dynamics (CFD). It is important to find costeffective and accurate methods to solve such systems. Iterative methods are among those widely used, especially for 3D problems. In this project, we consider iterative methods which are especially suited to massively parallel architectures. To accelerate convergence of these iterative methods, preconditioners are often used. Good preconditioners reduce the number of iterations and involves few arithmetic operations per iteration. Effective parallel preconditioners must account for the global coupling inherent in elliptic problems. On the other hand, efficient parallel implementation often favors local computations. Multiscale iterative methods represent a good compromise between these two conflicting goals. We focused our attention on two classes of multiscale preconditioners: multilevel basis preconditioners and domain decomposition preconditioners.
Descriptors : *MATHEMATICAL MODELS, *PARALLEL PROCESSING, *ITERATIONS, ALGORITHMS, LINEAR SYSTEMS, GLOBAL, OPTIMIZATION, MATRICES(MATHEMATICS), GRIDS, ACCURACY, COMPUTATIONAL FLUID DYNAMICS, MESH, MATHEMATICAL PROGRAMMING, THREE DIMENSIONAL, FINITE DIFFERENCE THEORY, NONLINEAR SYSTEMS, BOUNDARY VALUE PROBLEMS, LEAST SQUARES METHOD, CONVERGENCE, HEURISTIC METHODS, DECOMPOSITION, ARITHMETIC.
Subject Categories : Numerical Mathematics
Operations Research
Distribution Statement : APPROVED FOR PUBLIC RELEASE