Abstract
Many programming models for massively parallel machines exist, and each has its advantages and disadvantages. In this article we present a programming model that combines features from other programming models that (1) can be efficiently implemented on present and future Cray Research massively parallel processor (MPP) systems and (2) are useful in constructing highly parallel programs. The model supports several styles of programming: message-passing, data parallel, global address (shared data), and work-sharing. These styles may be combined within the same program. The model includes features that allow a user to define a program in terms of the behavior of the system as a whole, where the behavior of individual tasks is implicit from this systemic definition. (In general, features marked as shared are designed to support this perspective.) It also supports an opposite perspective, where a program may be defined in terms of the behaviors of individual tasks, and a program is implicitly the sum of the behaviors of all tasks. (Features marked as private are designed to support this perspective). Users can exploit any combination of either set of features without ambiguity and thus are free to define a program from whatever perspective is most appropriate to the problem at hand.