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

It is appropriate to repeat that the set of criteria we give are subjective but based on good
reasoning. For example, Marc Geilen and Twan Basten proposed a different set of criteria
in a recent paper [18]. They replaced condition 2 with an output completeness condition,
i.e., each signal should eventually converge to that prescribed by the denotational
semantics. They observed that Parks’ algorithm [16] cannot solve so-called local
deadlock problem where several actors are deadlocked in a cyclic fashion but the whole
model can still make progress, therefore Parks’ algorithm won’t try to solve these
deadlocks since it will only do so when the whole model is deadlocked. Thus the
execution will violate their completeness condition. Since they cannot come up with a
scheduler that would satisfy their criteria for all models, their solution is to give a
executing strategy that would satisfy their criteria for a subset of models, which are
bounded and effective. (Their definition for effectiveness is that every token produced
will be ultimately consumed.) However, due to Turing-completeness, whether or not a
model is bounded and effective is undecidable. Therefore there is no program which can
classify any arbitrary model before execution. And if we prefer completeness over
boundedness, some models may run out of memory, whereas using our criterion they
may be executed forever in bounded memory. We prefer the our way because it allows
useful functions provided by some part of the model to be performed in bounded memory
whereas with Geilen & Basten’s technique the model may have to be aborted at some
point due to memory constraints. For example, the model in Figure 3.1 can be executed
as the model in Figure 3.2 with bounded memory using our criterion. If we insist on
complete execution, then we will run out of the memory eventually. So this is a
something-better-than-nothing philosophy.
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