2. A code generator for the CAL actor language Lars Wernli Supervisor: Joern Janneck, UC Berkeley Professor: Lothar Thiele, ETH Zuerich

3. What is Ptolemy II? continuous time finite-state machine discrete time Hierarchical, heterogeneous model

4. Actor oriented design input portsoutput ports parameters Actor tokens ‘C’ 31 ‘L’ ‘A’ tokens 42 ‘P’ 9912‘\’ state 42 Actors decouple data and control N Data 41 FIRE

5. Actor oriented design Actors decouple data and control input portsoutput ports parameters Actor token 1 tokens 2 ‘P’ 9912‘\’ state 45 N Data 4 45 4142 ‘C’‘A’‘L’

6. Actors in Ptolemy II Firing is divided into three phases: prefire()1 time –checks whether action can fire or not fire()n times –calculates output tokens postfire() 0 or 1 times –updates persistent state

7. Writing a Ptolemy actor int sum = 0, _sum; prefire() { return N.hasToken(); } fire() { _sum = sum; int n = N.getToken(); if (Data.hasTokens(n)) { _sum = _sum + n; for (int i = 0; i < n; i++) Out2.putToken(Data.getToken()); Out1.putToken(_sum); } else { // what to do with the value of n? } postfire() { sum = _sum; }

8. Writing a Ptolemy actor int sum = 0, _sum; prefire() { return N.hasToken(); } fire() { _sum = sum; int n = N.getToken(); if (Data.hasTokens(n)) { _sum = _sum + n; for (int i = 0; i < n; i++) Out2.putToken(Data.getToken()); Out1.putToken(_sum); } else { // what to do with the value of n? } postfire() { sum = _sum; }

9. What is CAL? CAL is a textual language for writing dataflow actors. Integer sum := 0; action N:[n], Data:[d] repeat n ==> Out1:[sum], Out2:[d] repeat n do sum := sum + n; end The actor just introduced written in CAL:

10. Motivation for using CAL makes writing actors more accessible reduces amount of code to be written reduces error probability allows information extraction for model analysis CAL actors may be reused by other platforms, or new versions of Ptolemy

11. Design goal for CAL CAL is intended to be retargeted to a variety of platforms make retargeting as simple as possible –modular compiler design –modular code generation

12. CAL compilation —the big picture. CAL CalCore CAL (0) CAL (n) parsing CAL (1) transformation, annotation code generation source text Caltrop AST target platform Ptolemy IIMosesPålsjö/KoalaJGrafChartLegOS Java platforms C platforms

13. Generic and specific actor CalCore generic code generator platform specific code generator Code generator is easy to retarget Actor core can be reused by other platforms

14. Code generator and target code design Design goals 1.Make retargeting the code generator as simple as possible 2.Reusability of generated code 3.Optimize for speed Challenges -specify an interface for generic part of the actor -matching the generic actor interface to Ptolemy API

15. State shadowing Problem: state changing firing in CAL vs state-invariant fire() in Ptolemy generic variable interface Ptolemy specific variable object State:Shadow State: fire() { listener.rollbackAll(); … } postfire() { … listener.commitAll(); } change listener 42 assign(45) 45 markAsChanged(this)

16. State shadowing Problem: state changing firing in CAL vs state-invariant fire() in Ptolemy generic variable interface Ptolemy specific variable object State:Shadow State: fire() { listener.rollbackAll(); … } postfire() { … listener.commitAll(); } change listener 4245 rollbackAll()rollback()

17. State shadowing Problem: state changing firing in CAL vs state-invariant fire() in Ptolemy generic variable interface Ptolemy specific variable object State:Shadow State: fire() { listener.rollbackAll(); … } postfire() { … listener.commitAll(); } change listener 42 assign(47) 47 markAsChanged(this)

18. State shadowing Problem: state changing firing in CAL vs state-invariant fire() in Ptolemy generic variable interface Ptolemy specific variable object State:Shadow State: fire() { listener.rollbackAll(); … } postfire() { … listener.commitAll(); } change listener 4247

19. State shadowing Problem: state changing firing in CAL vs state-invariant fire() in Ptolemy generic variable interface Ptolemy specific variable object State:Shadow State: fire() { listener.rollbackAll(); … } postfire() { … listener.commitAll(); } change listener 4247 commitAll() 47 commit()

20. State shadowing Problem: state changing firing in CAL vs state-invariant fire() in Ptolemy generic variable interface Ptolemy specific variable object State:Shadow State: fire() { listener.rollbackAll(); … } postfire() { … listener.commitAll(); } change listener 4247

21. Achievements code generation for full-fledged language -higher-order function closures -procedural closures -set/list/map comprehensions -input port patterns -regular action selectors -… reusability of generated code code generator easy to retarget to other Java platforms

22. Achievements generated actors run with acceptable speed facilitate retargeting to other languages (such as C) –design template for code generators Pålsjö/Koala LTH –reusable infrastructure

23. Future work –Implement type checking –Describe the transformations on the AST in XML –Retarget the code generator to other platforms (LegOS UCB, Moses ETH?) –Model compilation using CAL actor Network + actors  schedule Network + actors + schedule  actor

24. It’s time for a demo

25. Atomic actors in Ptolemy implemented in Java domain polymorph ports parameters split-phase-firing: –prefire() –fire() –postfire()

26. Atomic actors in Ptolemy implemented in Java domain polymorph ports parameters split-phase-firing: –prefire() –fire() –postfire()

27. The Ptolemy II GUI

28. Models in Ptolemy II actor based heterogeneous systems hierarchical composite actors treated like atomic directors decouple behavior & control flow

29. Writing Ptolemy actors in Java....requires certain knowledge about the Ptolemy II API..results in platform specific classes..is error-prone..is often redundant..makes it hard to extract information from the actors Specifying actors in Java is problematic

30. Writing Ptolemy actors in Java....requires certain knowledge about the Ptolemy II API..results in platform specific classes..is error-prone..is often redundant..makes it hard to extract information from the actors Specifying actors in Java is problematic

31. A better approach We should be able to generate actors from a more abstract description. Benefits: –makes writing actors more accessible –actors may be retargeted to other platforms, or new versions of Ptolemy –reduces error probability –reduces amount of code to be written –actors get more readable and analyzable

32. Can you guess what this does? actor B () Double Input ==> Double Output: Integer n := 0; Double sum := 0; action [a] ==> [sum / n] DO n := n + 1; sum := sum + a; end

33. Can you guess what this does? actor B () Double Input ==> Double Output: Integer n := 0; Double sum := 0; action [a] ==> [sum / n] : n := n + 1; sum := sum + a; end

34. What about this? actor PrimeSieve () Integer Input ==> Integer Output: [Integer --> Boolean] filter := lambda (Integer a) --> Boolean : false end; function divides (Integer a, Integer b) --> Boolean : b mod a = 0 end action [a] ==> [] guard filter(a) end action [a] ==> [a] guard not filter(a) var [Integer --> Boolean] f = filter do filter := lambda(Integer b) --> Boolean: f(b) or divides(a, b) end; end

35. ActorCore vs Ptolemy API state management –fire vs fire n / postfire –state changing computation vs state-invariant fire input ports –random access to input channels vs sequential read methods

36. The runtime environment 1.Variable objects & change listener –Support state shadowing –Provide a generic interface to the Ptolemy Token and Parameter objects 2.Port wrappers –Emulate random access input ports –Provide a generic interface to the Ptolemy TypedIOPorts  Factory –Creates wrapping objects –facilitates decoupling between ActorCore and Ptolemy API

37. Three implementation details Actors at runtime 1.How the PtActor passes Ptolemy objects to the ActorCore via factory 2.How CAL scopes are represented in the ActorCore The code generator 3.How the code generator uses the visitor pattern to traverse the AST

38. 1. Actors and the Factory

39. actor DeadlockPrimeSieve () Integer Input ==> Integer Output: [Integer --> Boolean] filter := lambda (Integer a) --> Boolean : false end; action [a] ==> [a] guard not filter(a) var [Integer --> Boolean] f = filter do filter := lambda(Integer b) --> Boolean: f(b) or (lambda (Integer a, Integer b)--> Boolean : b mod a = 0; end)(a, b) end 2. CAL scopes

40. 2. Structure of the ActorCore

41. accept(this) 3. The visitor pattern e.argTuple.accept(this); // generate some code … e.function.accept(this); // generate more code … visitApplication(this) visitor.visitTuple(this);visitor.visitApplication(this);

42. Problems solved matching CAL to Ptolemy –single atomic action vs prefire / fire n / postfire –state changing computation vs state-invariant fire –CalCore scopes vs Java scopes –random access to channels vs sequential read methods

43. Further work –Implement type checking –Describe the transformations on the AST in XML –Network + actors  schedule –Network + actors + schedule  actor –Retarget the code generator to other platforms (Moses ETH)

44. continuous time finite-state machine discrete time Hierarchical, heterogeneous model

45. Generic and specific code generator

46. The CAL compiler