Published online by Cambridge University Press: 06 August 2010
Abstract
Dataflow models of computation have intrigued computer scientists since the 1970s. They were first introduced by Jack Dennis as a basis for parallel programming languages and architectures, and by Gilles Kahn as a model of concurrency. Interest in these models of computation has been recently rekindled by the resurrection of parallel computing, due to the emergence of multicore architectures. However, Dennis and Kahn approached dataflow very differently. Dennis' approach was based on an operational notion of atomic firings driven by certain firing rules. Kahn's approach was based on a denotational notion of processes as continuous functions on infinite streams. This paper bridges the gap between these two points of view, showing that sequences of firings define a continuous Kahn process as the least fixed point of an appropriately constructed functional. The Dennis firing rules are sets of finite prefixes satisfying certain conditions that ensure determinacy. These conditions result in firing rules that are strictly more general than the blocking reads of the Kahn–MacQueen implementation of Kahn process networks, and solve some compositionality problems in the dataflow model. This work was supported in part by the Center for Hybrid and Embedded Software Systems (CHESS) at UC Berkeley, which receives support from the National Science Foundation (NSF awards #0720882 (CSR-EHS: PRET), #0647591 (CSR-SGER), and #0720841 (CSR-CPS)), the US Army Research Office (ARO #W911NF-07-2-0019), the US Air Force Office of Scientific Research (MURI #FA9550-06-0312 and AF-TRUST #FA9550-06-1-0244), the Air Force Research Lab (AFRL), the State of California Micro Program, and the following companies: Agilent, Bosch, DGIST, National Instruments, and Toyota.
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