Data parallel languages, like High Performance Fortran (HPF), support the notion of distributed arrays. However, the implementation of such distributed array structures and their access on message passing computers is not straightforward. This holds especially for distributed arrays that are aligned to each other and given a block-cyclic distribution. In this paper, an implementation framework is presented for HPF distributed arrays on message passing computers. Methods are presented for efficient (in space and time) local index enumeration, local storage, and communication. Techniques for local set enumeration provide the basis for constructing local iteration sets and communication sets. It is shown that both local set enumeration and local storage schemes can be derived from the same equation. Local set enumeration and local storage schemes are shown to be orthogonal, i.e., they can be freely combined. Moreover, for linear access sequences generated by our enumeration methods, the local address calculations can be moved out of the enumeration loop, yielding efficient local memory address generation. The local set enumeration methods are implemented by using a relatively simple general transformation rule for absorbing ownership tests. This transformation rule can be repeatedly applied to absorb multiple ownership tests. Performance figures are presented for local iteration overhead, a simple communication pattern, and storage efficiency.

This paper describes a translation method for the automatic parallelization of programs based on a separately specified representation of the data. The method unifies the concept of data-representation on the algorithm-level as well as machine-level, based on the so-called view concept. It is shown that given a decomposition of the data, application of the translation method to the view-based Booster programming language results in efficient SPMD-code for distributed- as well as shared-memory architectures. It will be argued that the method is not restricted to Booster, but can also be applied to other languages.