Edwin M. R. M. Paalvast

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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(More)
abstract Data decomposition is probably the most successful method for generating parallel programs. In this paper a general framework is described for the automatic generation of parallel programs based on a separately specified decomposition of the data. To this purpose, programs and data decompositions are expressed in a calculus, called V-cal. It is(More)
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(More)
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