Dynamic Dataflow Modeling in Ptolemy II
Gang Zhou
Technical Memorandum UCB/ERL M05/2,
University of California, Berkeley, CA 94720, December 21, 2004.
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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.
