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

{ ( [ 1 ] , [ 0 ] , ⊥), ( [ 0 ] , ⊥, [ 1 ] ) , ( ⊥ , [ 1 ] , [ 0]
) }
A blocking read on any input port would not give the desired behavior. An actor with this
set of firing rules can not be used in the current DDF implementation.
3.4 Performance Comparison: SDF, DDF and PN
Dataflow process networks have been shown to be a special case of Kahn process
networks. 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 or at run time. SDF is scheduled at compile time.
After the static schedule of an SDF graph is computed, the actors can be fired according
to the predetermined sequence specified by the schedule. DDF is scheduled at run time,
which is much more expensive.
To compare the performance of the SDF, DDF and PN domains, we simulated one SDF
model under all three domains and plotted the running time in each domain. The model
was slightly modified from ViterbiDecoder created by Rachel Zhou and Edward Lee so
that we can better control the number of iterations in each domain. We also replaced
original MonitorValue actors with Discard actors so that their graphical displays would
not slow down the simulation. The final model contains 30 actors. As we can see from
Figure 3.6, the simulation in the PN domain takes about twice as long as that in the DDF
domain, and both are proportional to the number of iterations. SDF has an upfront cost
40