A Transformation Framework for Communicating Multiprocessor-Tasks

Parallel programming models based on a mixture of task and data parallelism have shown to be successful in addressing the increasing communication overhead of distributed memory platforms with a large number of processors. In these models, an application is decomposed into a set of parallel tasks that can run on an arbitrary number of processors. The communication between different tasks is allowed only at the start and the end of a task, thus limiting the possible communication patterns and the potential granularity of the tasks. In this paper, we consider an extended parallel programming model that additionally supports communication between running parallel tasks. We describe a specification language for applications in the new programming model and propose a transformation framework for a step-wise derivation of an executable message passing program from the specification language. The advantages of the approach are demonstrated for solution methods for ordinary differential equations.