Algorithmic skeletons for adaptive multigrid methods

Abstract

This paper presents a new approach to parallel programming with algorithmic skeletons, i.e. common parallelization patterns. We use an imperative language enhanced by some functional features as host for the embedding of the skeletons. This allows an efficient implementation and at the same time a high level of programming. In particular, low level communication problems such as deadlocks are avoided. Both data and process parallel skeletons can be used, but the emphasis is placed on the first category. By defining data parallel skeletons for dynamic data structures, we obtain constructs for handling problems with irregular and/or dynamic character. The implementation of an adaptive multigrid algorithm illustrates how such problems can be solved by using these constructs.