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Back 2 Basics with Proto

Back 2 Basics with Proto. Jonathan Bachrach MIT AI Lab. Proto. Goals Examples Relation Definition State Future. Proto Hello World. (format out “hello world”). Simple Productive Powerful Extensible Dynamic Efficient Real-time. Teaching and research vehicle

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Back 2 Basics with Proto

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  1. Back 2 Basics with Proto Jonathan Bachrach MIT AI Lab

  2. Proto • Goals • Examples • Relation • Definition • State • Future

  3. Proto Hello World (format out “hello world”)

  4. Simple Productive Powerful Extensible Dynamic Efficient Real-time Teaching and research vehicle Electronic music is domain to keep it honest Proto Goals

  5. Simplicity • 10K lines 10 page manual • Hard limit – pressure makes pearls

  6. Best of All Worlds • Want scripting and delivery language rolled into one • Tools work better • No artificial boundaries and cliffs • Never been done effectively • Electronic music forces realism

  7. Proto Ancestors • Language Design is Difficult • Leverage proven ideas • Make progress in selective directions • Ancestors • Scheme • Cecil • Dylan

  8. Concise naming Procedural macros Objects all the way Long-winded naming Rewrite rule only Only records Proto <=> Scheme

  9. Prefix syntax Scheme inspired special forms Infix syntax Smalltalk inspired special forms Proto <=> Cecil

  10. Prefix syntax Prototype-based Procedural macros Rationalized collection protocol / hierarchy Always open Predicate types Infix syntax Class-based Rewrite-rule only … Conflated collection protocol / hierarchy Sealing Fixed set of types Proto <=> Dylan

  11. Object Orientation • Assume you know OO basics • Motivations: • Abstraction • Reuse • Extensibility

  12. Prototype-based OO • Simplified object model • No classes • Cloning basic operation for instance and prototype creation • Prototypes are special objects that serve as classes • Inheritance follows cloned-from relation

  13. Proto: OO & MM (dv <point> (isa <any>)) (slot <point> (point-x <int>) 0) (slot <point> (point-y <int>) 0) (dv p1 (isa <point>)) (dm + ((p1 <point>) (p2 <point>) => <point>) (isa <point> (set point-x (+ (point-x p1) (point-x p2))) (set point-y (+ (point-y p1) (point-y p2))))

  14. Language Design:User Goals -- The “ilities” • Learnability • Understandability • Writability • Modifiability • Runnability • Interoperability

  15. Learnability • Simple • Small • Regular • Gentle learning curve • Perlis: “Symmetry is a complexity reducing concept…; seek it everywhere.”

  16. Proto: Learnability • Simple and Small: • 16 special forms: if, seq, set, fun, let, loc, lab, fin, dv, dm, dg, isa, slot, ds, ct, quote • 7 macros: try, rep, mif, and, or, select, case • Gentle Learning Curve: • Graceful transition from functional to object-oriented programming • Perlis: “Purely applicative languages are poorly applicable.”

  17. Proto: Special Forms IF (IF ,test ,then ,else) SEQ (SEQ ,@forms) SET (SET ,name ,form) | (SET (,name ,@args) ,form) LET (LET ((,var ,init) …) ,@body) FUN (FUN ,sig ,@body) LOC (LOC ((,name ,sig ,@body) …) .@body) LAB (LAB ,name ,@body) FIN (FIN ,protected-form ,@cleanup-forms) DV (DV ,var ,form) DM (DM ,name ,sig ,@body) DG (DG ,name ,sig) ISA (ISA (,@parents) ,@slot-inits) SLOT (SLOT ,owner ,var ,init) sig (,@vars) | (,@vars => ,var) var ,name | (,name ,type) slot-init (SET ,name ,value)

  18. Understandability • Natural notation • Simple to predict behavior • Modular • Models application domain • Concise

  19. Proto: Understandability • Describable by a small interpreter • Size of interpreter is a measure of complexity of language • Regular syntax • Debatable whether prefix is natural, but it’s simple, regular and easy to implement

  20. Writability • Expressive features and abstraction mechanisms • Concise notation • Domain-specific features and support • No error-prone features • Internal correctness checks (e.g., typechecking) to avoid errors

  21. Proto: Error Proneness • No out of language errors • At worst all errors will be be caught in language at runtime • At best potential errors such as “no applicable methods” will be caught statically earlier and in batch • Unbiased dispatching and inheritance • Example: Method selection not based on lexicographical order as in CLOS

  22. Design Principle Two:Planned Serendipity • Serendipity: • M-W: the faculty or phenomenon of finding valuable or agreeable things not sought for • Orthogonality • Collection of few independent powerful features combinable without restriction • Consistency

  23. Proto: Serendipity • Objects all the way down • Slots accessed only through calls to generic’s • Simple orthogonal special forms • Expression oriented • Example: • Exception handling can be built out of a few special forms: lab, fin, loc, …

  24. Modifiability • Minimal redundancy • Hooks for extensibility included automatically • Users equal partner in language design • No features that make it hard to change code later

  25. Proto: Extensible Syntax • Syntactic Abstraction • Procedural macros • WSYWIG • Pattern matching • Code generation • Example: (ds (unless ,test ,@body) `(if (not ,test) (seq ,@body)))

  26. Proto: Multimethods • Can add methods outside original class definition: • (dm jb-print ((x <node>)) …) • (dm jb-print ((x <str>)) …)

  27. Proto: Generic Accessors • All slot access goes through generic function calls • Can easily redefine these generic’s without affecting client code

  28. Runnability • Features for programmers to control efficiency • Analyzable by compilers and other tools

  29. Proto: Optional Types • All bindings and parameters can take optional types • Rapid prototype without types • Add types for documentation and efficiency • Example: (dm format (s msg (args …)) …) (dm format ((s <stream>)(msg <str>) (args …)) …)

  30. Proto: Pay as You Go • Don’t charge for features not used • Pay more for features used in more complicated ways • Examples: • Dispatch • Just function call if method unambiguous from argument types • Otherwise require dynamic method lookup • Proto’s bind-exit called “lab” • Local exits are set + goto • Non local exits must create a frame and stack alloc an exit closure

  31. The Rub • Support for evolutionary programming creates a serious challenge for implementers • Straightforward implementations would exact a tremendous performance penalty

  32. Implementation Strategy • Simple dynamic compilation • Maintains both • optimization and • interactivity

  33. Initial Loose Compilation • Very quick compilation • Generate minimal dependencies • only names and macros

  34. Dynamic Whole Program Compilation • Assume complete information • Perform aggressive type flow analysis • Chooses, clones and inlines methods • Compilation can be triggered manually, through dependencies, or through feedback

  35. Dependency Tracking • Assumptions are tracked • Changed assumptions trigger recompilation • Based on Fun-O-Dylan approach • Dependencies logged on bindings • Record dependent and compilation stage

  36. Simple Code Generator • Focus is on high-level optimizations • Potentially gen-code direct from AST with approximated peep-hole optimizations

  37. Save Image • Save executable copy of image to disk • Maintains optimizations and dependencies • Uses dump/undump approach of emacs • Avoid hassles of • File formats • Databases • etc

  38. Status • Fully bootstrapped • Module system on line by week’s end • Native code-gen in progress • Dependency tracking by summer’s end • Flow-typist by summer’s end

  39. Language Design Dynamic parameterized types Dynamic Interfaces Series Macros Language Implementation Dynamic compilation Analysis/optimizations Visualization Real-time Research Directions

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