DataScalar: A memory-centric approach to computing

Algorithms from scientific computing often exhibit a two-level parallelism based on potential method parallelism and potential system parallelism. We consider the parallel implementation of those algorithms on distributed memory machines. The two-level potential parallelism of algorithms is expressed in a specification consisting of an upper level hierarchy of multiprocessor tasks each of which has an internal structure of uniprocessor tasks. To achieve an optimal parallel execution time, the parallel execution of such a program requires an optimal scheduhng of the multiprocessor tasks and an appropriate treatment of uniprocessor tasks. For an important subclass of structured method parallelism we present a scheduling methodology which takes data redistributions between multiprocessor tasks into account. As costs we use realistic parallel runtimes. The scheduling methodology is designed for an integration into a parallel compiler tool. We illustrate the multitask scheduling by several examples from numerical analysis. © 1999 Elsevier Science B.V. All rights reserved.