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Dataflow graphs are described as a machine language for parallel machines. Static and dynamic dataflow architectures are presented as two implementations of the abstract dataflow model. Static dataflow allows at most one token per arc in dataflow graphs and thus only approximates the abstract model where unbounded token storage per arc is assumed. Dynamic architectures tag each token and keep then in a common pool storage, thus permitting a better approximation of the abstract model. The relative merits of the two approaches are discussed. Functional data structures and I-structures are presented as two views of data structures which are both compatible with the dataflow model. These views are contrasted and compared in regard to efficiency and exploitation of potential parallelism in programs. A discussion of major dataflow projects and a prognosis for dataflow architectures are also presented.
Arvind et al. (Sun,) studied this question.