1. Model Transformation with the Ptera Controller
Thomas Huining Feng
CHESS, EECS, UC Berkeley

2. Motivating Sinewave Example
An SDF model to generate a sine wave with
Automatic partial evaluation improves performance
sin(
2πFn ) Fs

3. Intuition of Model Transformation
Pattern matching – find a context-free part of the model

4. Intuition of Model Transformation
Pattern matching – find a context-free part of the model
Replace it with a more compact design (value statically computed)

5. Intuition of Model Transformation
Pattern matching – find a context-free part of the model
Replace it with a more compact design (value statically computed)
Continue with similar transformations until fixpoint

6. Ptera (Ptolemy Event Relationship Actor) Workflow
RemoveMultiply
SimplifyMultiply
SimplifyDivide

7. Syntax and Semantics for Ptera
Based on event graphs [Schruben 1983]
Visual representation
Nodes are events
Edges are scheduling relations
Variable
Final Event
Action
Guard
Initial Event
Delay

8. Syntax and Semantics of Ptera
Based on event graphs [Schruben 1983]
Visual representation
Nodes are events
Edges are scheduling relations
Execution with an unbounded event queue
0.0
1.0
1.0
1.0
1.0
End
Increase
Start
Increase
Increase …
Increase
0.0
1.0
2.0
10.0
10.0
time

9. Hierarchical Model Each event can be associated with a submodel
A submodel is itself a model
No difference in syntax
Conceptually equipped with a separate event queue
Model time is global
When Compute occurs at time 1, the submodel starts
When the submodel ends at time t (t ≥ 2), Compute is finished
End is processed at time t + 1

10. Events for Model Transformation
A simple workflow for repeating a transformation

11. The Sinewave Optimizer
RemoveMultiply
SimplifyMultiply
SimplifyDivide

12. The Sinewave Optimizer
Hierarchical workflow breaks down complexity
All transformation rules access the shared Model variable. No need for messaging.
Order of the 3 transformations is unimportant (but a deterministic order exists)
When model time is synchronized with real time, execution produces animation

13. Conclusion Transformation rule specified in the modeling language
The control is essentially a model (so we call it model-based transformation)
The Ptera model of computation
Supports hierarchical workflows
Better expressiveness than state machines and control flow diagrams
Shares data with variables instead of messages
Permits heterogeneous composition (with DE, FSM, dataflow, etc)
Applications
Model optimization
Model construction
Design refactoring
Ptera can also simulate discrete-event systems
Available from Ptolemy II 8.0 More demos in ptolemy/actor/gt/demo, ptolemy/domains/ptera/demo