Domain Decomposition A Bridge Between Nature And Parallel Computers

Domain Decomposition: a Bridge Between Nature and Parallel Computers

Domain-Based Parallelism and Problem Decomposition Methods in Computational Science and Engineering

This volume is one attempt to provide cross-disciplinary communication between heterogeneous computational groups developing solutions to problems of parallelization.

Domain Decomposition

Domain decomposition is an intuitive organizing principle for a partial differential equation (PDE) computation, both physically and architecturally. However, its significance extends beyond the readily apparent issues of geometry and discretization, on one hand, and of modular software and distributed hardware, on the other. Engineering and computer science aspects are bridged by an old but recently enriched mathematical theory that offers the subject not only unity, but also tools for analysis and generalization. Domain decomposition induces function-space and operator decompositions with valuable properties. Function-space bases and operator splittings that are not derived from domain decompositions generally lack one or more of these properties. The evolution of domain decomposition methods for elliptically dominated problems has linked two major algorithmic developments of the last 15 years: multilevel and Krylov methods. Domain decomposition methods may be considered descendants of both classes with an inheritance from each: they are nearly optimal and at the same time efficiently parallelizable. Many computationally driven application areas are ripe for these developments. A progression is made from a mathematically informal motivation for domain decomposition methods to a specific focus on fluid dynamics applications. To be introductory rather than comprehensive, simple examples are provided while convergence proofs and algorithmic details are left to the original references; however, an attempt is made to convey their most salient features, especially where this leads to algorithmic insight. Keyes, David E. Unspecified Center NAS1-18605; NAS1-19480; NSF ECS-89-57475; RTOP 505-90-52-01...