Partitioning of Unstructured Problems for Parallel Processing Horst

Many large scale computational problems are based on unstruc-tured computational domains. Primary examples are unstructured grid calculations based on finite volume methods in computational fluid dynamics, or structural analysis problems based on finite element approximations. Here we will address the question of how to distribute such unstructured computational domains over a large number of processors in a MIMD machine with distributed memory. A graph theoretical framework for these problems will be established. Based on this framework three decomposition algorithms will be introduced. In particular a new decomposition algorithm will be discussed, which is based on the computation of an eigenvector of the Laplacian matrix associated with the graph. Numerical comparisons on large scale two and three dimensional problems demonstrate the superiority of the new spectral bisection algorithm.