Dynamic Dataflow Modeling in Ptolemy II - Ptolemy Project ...

One of the main objectives of the Ptolemy project is to experiment with different models
of computation (called domains). The domains currently implemented in Ptolemy II
include: Component Interaction (CI), Communicating Sequential Process (CSP),
Continuous Time (CT), Distributed Discrete Event (DDE), Dynamic Dataflow (DDF),
Discrete Event (DE), Discrete Time (DT), Finite State Machines (FSM), Giotto, Graphics
(GR), Heterochronous Dataflow (HDF), Process Networks (PN), Parameterized
Synchronous Dataflow (PSDF), Synchronous Dataflow (SDF), Synchronous Reactive
(SR), and Timed Multitasking (TM). An essential difference between concurrent models
of computation is their modeling of time [1]. Some are very explicit by taking time to be
a real number that advances uniformly, and placing events on a time line or evolving
continuous signals along the time line (such as CT, DE). Others are more abstract and
take time to be discrete (such as DT, SR). Others are still more abstract and take time to
be merely a constraint imposed by causality (such as PN, CSP, SDF, DDF). The last
interpretation results in time that is partially ordered, which provides a mathematical
framework for formally analyzing and comparing models of computation [3].
This report is mainly about one particular model of computation ----- dynamic dataflow
(DDF). It describes and implements a DDF scheduling algorithm under the Ptolemy II
framework. The following gives an outline of this report. In the remainder of section 1,
we further motivate the use of dataflow MoC and briefly describe the Ptolemy II software
environment upon which the DDF domain is created. Section 2 gives a review of
denotational semantics of Kahn process networks, which is the theoretical foundation of
any dataflow process network. Since Kahn process networks lack the notion of firing, we
outline the treatment by Lee [8], which defines a continuous Kahn process as the least
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