Integrating Data and Task Parallelism in Scientific Programs

Abstract

Functional languages attract the attention of developers of parallelizing compilers because of the implicit parallelism of functional programs and the simplified data dependence analysis of functional statements. A major drawback of functional languages is that naive translation of functional programs results in code that requires excessive memory. In this paper we explore the connection between the memory optimization and communication optimization of parallel codes generated from functional languages. We also show how a functional language can be used as an intermediate form in the translation from FORTRAN to customized, architecture-specific parallel code.