Tagged-Token Data-Flow for Skeletons

Abstract

This paper discusses the use of a tagged-token macro data-flow execution model for implementing skeletons within the SKIPPER parallel programming environment. We show that it provides a suitable implementation model for skeletons involving runtime-bounded iterations and/or recursion such as data and task farms, especially in the presence of nesting. The new version of SKIPPER relies on a custom data-flow interpreter controlling SPMD based parallelism. Input data-flow graphs are obtained from a skeletal program specification written in CAML and making use of user defined sequential functions written in C. Initial evaluation suggests that performance close to handcrafted C with MPI can be achieved and that the support for nesting does not entail a significant performance penalty.