Using Algorithmic Skeletons with Dynamic Data Structures

Algorithmic skeletons are polymorphic higher-order functions representing common parallelization patterns. A special category are data-parallel skeletons, which perform operations on a distributed data structure. In this paper, we consider the case of distributed data structures with dynamic elements. We present the enhancements necessary in order to cope with these data structures, both on the language level and in the implementation of the skeletons. Further, we show that these enhancements practically do not aaect the user, who merely has to supply two additional functional arguments to the communication skeletons. We then implement a parallel sorting algorithm using dynamic data with the enhanced skeletons on a MIMD distributed memory machine. Run-time measurements show that the speedups of the skeleton-based implementation are comparable to those obtained for a direct C implementation .