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

Abstract
Dataflow process networks are a special case of Kahn process networks (PN). In dataflow
process networks, each process consists of repeated firings of a dataflow actor, which
defines a quantum of computation. Using this quantum avoids the complexities and
context switching overhead of process suspension and resumption incurred in most
implementations of Kahn process networks. Instead of context switching, dataflow
process networks are executed by scheduling the actor firings. This scheduling can be
done at compile time for synchronous dataflow (SDF) which is a particularly restricted
case with the extremely useful property that deadlock and boundedness are decidable.
However, for the most general dataflow, the scheduling has to be done at run time and
questions about deadlock and boundedness cannot be statically answered. This report
describes and implements a dynamic dataflow (DDF) scheduling algorithm under
Ptolemy II framework based on original work in Ptolemy Classic. The design of the
algorithm is guided by several criteria that have implications in practical implementation.
We compared the performance of SDF, DDF and PN. We also discussed composing DDF
with other models of computation (MoC). Due to Turing-completeness of DDF, it is not
easy to define a meaningful iteration for a DDF submodel when it is embedded inside
another MoC. We provide a suite of mechanisms that will facilitate this process. We give
several application examples to show how conditionals, data-dependent iterations,
recursion and other dynamic constructs can be modeled in the DDF domain.
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