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

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

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

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

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

Ptera (Ptolemy Event Relationship Actor) Workflow RemoveMultiply SimplifyMultiply SimplifyDivide

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

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

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

Events for Model Transformation A simple workflow for repeating a transformation

The Sinewave Optimizer RemoveMultiply SimplifyMultiply SimplifyDivide

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

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