April 16, 20098th Biennial Ptolemy Miniconference

Model TransformationModel Transformationwith the Ptera Controllerwith the Ptera Controller

Thomas Huining Feng

CHESS, EECS, UC Berkeley

Ptolemy Miniconference, Thomas Feng, 04/16/2009

2 / 13

Motivating Sinewave ExampleMotivating Sinewave Example

An SDF model to generate a sine wave with

Automatic partial evaluation improves performance

Ptolemy Miniconference, Thomas Feng, 04/16/2009

3 / 13

Intuition of Model TransformationIntuition of Model Transformation

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

Ptolemy Miniconference, Thomas Feng, 04/16/2009

4 / 13

Intuition of Model TransformationIntuition of Model Transformation

Pattern matching – find a context-free part of the modelReplace it with a more compact design (value statically computed)

Ptolemy Miniconference, Thomas Feng, 04/16/2009

5 / 13

Intuition of Model TransformationIntuition of Model Transformation

Pattern matching – find a context-free part of the modelReplace it with a more compact design (value statically computed)Continue with similar transformations until fixpoint

Ptolemy Miniconference, Thomas Feng, 04/16/2009

6 / 13

Ptera (Ptolemy Event Relationship Actor) WorkflowPtera (Ptolemy Event Relationship Actor) Workflow

SimplifyMultiply SimplifyDivide

RemoveMultiply

Ptolemy Miniconference, Thomas Feng, 04/16/2009

7 / 13

Based on event graphs [Schruben 1983]Visual representation

Nodes are eventsEdges are scheduling relations

Syntax and Semantics for PteraSyntax and Semantics for Ptera

Action GuardDelay

Variable

Initial EventFinal Event

Ptolemy Miniconference, Thomas Feng, 04/16/2009

8 / 13

Syntax and Semantics of PteraSyntax and Semantics of Ptera

Based on event graphs [Schruben 1983]Visual representation

Nodes are eventsEdges are scheduling relations

Execution with an unbounded event queue

0.0 1.0 2.0 10.0 10.0

…Start

1.0

Increase Increase IncreaseEnd

Increase

1.0 1.0 1.0

0.0

time

Ptolemy Miniconference, Thomas Feng, 04/16/2009

9 / 13

Hierarchical ModelHierarchical Model

Each event can be associated with a submodelA submodel is itself a model

No difference in syntaxConceptually equipped with a separate event queueModel time is global

When Compute occurs at time 1, the submodel startsWhen the submodel ends at time t (t ≥ 2), Compute is finishedEnd is processed at time t + 1

Ptolemy Miniconference, Thomas Feng, 04/16/2009

10 / 13

Events for Model TransformationEvents for Model Transformation

A simple workflow for repeating a transformation

Ptolemy Miniconference, Thomas Feng, 04/16/2009

11 / 13

The Sinewave OptimizerThe Sinewave Optimizer

SimplifyMultiply SimplifyDivide

RemoveMultiply

Ptolemy Miniconference, Thomas Feng, 04/16/2009

12 / 13

The Sinewave OptimizerThe Sinewave Optimizer

Hierarchical workflow breaks down complexityAll 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

Ptolemy Miniconference, Thomas Feng, 04/16/2009

13 / 13

ConclusionConclusion

Transformation rule specified in the modeling language

The control is essentially a model(so we call it model-based transformation)

The Ptera model of computationSupports hierarchical workflowsBetter expressiveness than state machines and control flow diagramsShares data with variables instead of messagesPermits heterogeneous composition (with DE, FSM, dataflow, etc)

ApplicationsModel optimizationModel constructionDesign refactoringPtera can also simulate discrete-event systems

Available from Ptolemy II 8.0More demos in ptolemy/actor/gt/demo, ptolemy/domains/ptera/demo