Dataflow Process Networks

by Edward A. Lee and Thomas M. Parks

Two versions:


We review a model of computation used in industrial practice in signal processing software environments and experimentally in other contexts. We give this model the name "dataflow process networks," and study its formal properties as well as its utility as a basis for programming language design. Variants of this model are used in commercial visual programming systems such as SPW from the Alta Group of Cadence (formerly Comdisco Systems), COSSAP from Synopsys (formerly Cadis), the DSP Station from Mentor Graphics, and Hypersignal from Hyperception. They are also used in research software such as Khoros from the University of New Mexico and Ptolemy from the University of California at Berkeley, among many others.

Dataflow process networks are shown to be a special case of Kahn process networks, a model of computation where a number of concurrent processes communicate through unidirectional FIFO channels, where writes to the channel are non-blocking, and reads are blocking. In dataflow process networks, each process consists of repeated "firings" of a dataflow "actor". An actor defines a (often functional) quantum of computation. By dividing processes into actor firings, the considerable overhead of context switching incurred in most implementations of Kahn process networks is avoided.

We relate dataflow process networks to other dataflow models, including those used in dataflow machines, such as static dataflow and the tagged-token model. We also relate dataflow process networks to functional languages such as Haskell, and show that modern language concepts such as higher-order functions and polymorphism can be used effectively in dataflow process net works. A number of programming examples using a visual syntax are given.