1. From OODL Interpreters to VM’s and Beyond Jonathan Bachrach MIT AI Lab

2. Outline Material mostly from “Lisp In Small Pieces” Metacircular Interpreters CPS Interpreters Fast Interpreters VM’s and Compilation Speeding up VM’s VVM’s In Language VM’s Assignment #2

3. Lisp In Small Pieces Written by Christian Queinnec Highly recommend looking at this book Basis for a fair amount of today’s lecture

4. Q: Why Interpreters?

5. A: Why Interpreters? Semantics of language –is manifest –is measurable Allows more –Dynamism –Extensibility

6. Metacircular Interpreters Write interpreter in same language using own special forms Complexity of resulting interpreter is a measure of expressivity and simplicity of language Dream is to get a compiler for free from this!

7. Simple Environment (dv ) (dv (isa )) (slot (env-next )) (slot (env-frame ) (isa )) (dv $empty-env (isa )) (dm env-define-binding! ((env ) (name ) (value )) (set (elt (env-frame frame) name) value)) (dm env-extend! ((env ) (names ) (values ) => ) (let ((new-env (isa (set env-next env)))) (do2 (fun (name value) (set (elt (env-frame new-env) name) value)) names values) new-env)) (dm env-binding-value ((env ) (name ) => ) (if (== env $empty-env) nul (let ((value (elt (env-frame env) name))) (if (== value nul) (env-binding-value (env-next env) name) value))))

8. Proto in Proto (dm evol ((e ) (env )) e) (dm evol ((e ‘()) (env )) e) (dm evol ((e ) (env )) (env-binding-value env e)) (dm evol ((e ) (env )) (evol-list (head e) e env)) (dm evol-list ((_ 'quote) (e ) (env )) ( sexpr-text-of-quotation e))

9. Q: What’s the Problem? Why might a metacircular interpreter not be acceptable?

10. A: What’s the Problem? Creates bootstrapping issues Very inefficient

11. Tail Position / Calls If an expression is in tail position then it is not necessary to restore the environment –(fun (x) (f x)) –(fun (x) (if (f x) 5 6)) Tail calls are calls that are in tail position and don’t save/restore environment –Bind arguments –Goto function

12. Continuation Passing Style (CPS) CPS = Continuation Passing Style –Explicitly telling a computation where to send the result of that computation –Once computation is complete, executor applies receiver to result rather than returning it as a normal value Example –(fun (x) (f x)) => (fun (x k) (f’ x k))

13. CPS Interpreter Control is more explicit by passing continuations through interpreter Allows one to code complicated control forms without built-in support. –E.g., call/cc

14. CPS Interpreter Example (dm evol-list ((_ ‘quote) (e ) (env ) (k )) (k (sexpr-text-of-quotation e))) (dm evol-list ((_ 'if) (e ) (env ) (k )) (evol (sexpr-if-test e) env (fun (e) (if e (evol (sexpr-if-then e) env k) (evol (sexpr-if-else e) env k))))) (dm evol-seq+ ((e+ ) (env ) (k )) (if (empty? (tail e*)) (evol (head e*) env k) (evol (head e*) env (fun (e) (evol-seq+ (tail e*) env k))))) (dm evol-seq ((e* ) (env ) (k )) (if (empty? e*) (k #f) (evol-seq+ e* env k))) (dm evol-list ((_ 'seq) (e ) (env ) (k )) (evol-seq (sexpr-begin-actions e) env k))

15. CPS Wins (dm evol-list ((_ 'lab) (e ) (env ) (k )) (let ((exit (make-function '(x) `(,k x) env))) (evol-seq (sexpr-block-body e) (env-extend! env (lst (sexpr-block-name e)) (lst exit)) k)))

16. CPS Loses Problem is that some special forms like fin (cf. Lisp’s unwind-protect ) are difficult* to implement without resorting to built-in support –*It appears that they are possible with enough continuations Conses (allocates) lots of closures for continuations during interpretation

17. Interpreter Pretreatment Lighten up environment Invent beginnings of instruction set –Implemented in terms of thunks (zero parameter functions) Threaded as tree of calls to thunks Reject environment (rep’d as global *env*) Keep track of tail position

18. Fast Environment Globals stored in vector Locals are in list of inlined frames Model environment structure in static environment Resolve environment accesses to fixed or relative offsets –Globals are fixed offset –Locals are two relative offsets N.B., Can do even better by flattening ribcage environment into a stack

19. Pretreatment Pretreatment of programs is handled by the function meaning (compile) Converts into thunks that will serve as threading mechanism Each thunk can be thought of as an address to which we simply jump to execute

20. Inventing Instructions Use combinators which are like instructions for an abstract machine: (dm CONSTANT (value) (fun () value) ) (dm meaning-list ((_ 'quote) (e ) r t?) (fun () (CONSTANT (sexpr-text-of-quotation e)) ))

21. Interpreter Pretreatment Output (fun (n f k) (if (= n 0) (k 1) (f (- n 1) f (fun (r) (k (* n r)))))) (FIX-CLOSURE (ALTERNATIVE (CALL2 # (SHALLOW-ARGUMENT-REF 0) (CONSTANT 0)) (TR-REGULAR-CALL (SHALLOW-ARGUMENT-REF 2) (STORE-ARGUMENT (CONSTANT 1) (ALLOCATE-FRAME 1) 0) (TR-REGULAR-CALL (SHALLOW-ARGUMENT-REF 1) (STORE-ARGUMENT (CALL2 # (SHALLOW-ARGUMENT-REF 0) (CONSTANT 1)) (STORE-ARGUMENT (SHALLOW-ARGUMENT-REF 1) (STORE-ARGUMENT (FIX-CLOSURE (TR-REGULAR-CALL (DEEP-ARGUMENT-REF 1 2) (STORE-ARGUMENT (CALL2 # (DEEP-ARGUMENT REF 1 0) (SHALLOW-ARGUMENT-REF 0)) (ALLOCATE-FRAME 1) 0)) 1) (ALLOCATE-FRAME 2) 2) 1) 0))) 3)

22. Control Forms (dm ALTERNATIVE (m1 m2 m3) (fun () (if (m1) (m2) (m3))) ) (dm meaning-list ((_ 'if) (e ) r t?) (let ((m1 (meaning (sexpr-if-test e) r #f)) (m2 (meaning (sexpr-if-then e) r t?)) (m3 (meaning (sexpr-if-else e) r t?)) ) (ALTERNATIVE m1 m2 m3) ) )

23. Sequence (dm SEQUENCE (m m+) (fun () (m) (m+)) ) (dm meaning-sequence (e+ r t?) (if (empty? e+) (static-wrong "Illegal syntax: (seq)") (if (empty? (tail e+)) (meaning (head e+) r t?) (meaning*-sequence (head e+) (tail e+) r t?)))) (dm meaning*-sequence (e e+ r t?) (let ((m1 (meaning e r #f)) (m+ (meaning-sequence e+ r t?)) ) (SEQUENCE m1 m+) ) )

24. Closures (dm FIX-CLOSURE (m+ arity) (let ((arity+1 (+ arity 1))) (fun () (loc ((the-function (v* sr) (if (= (frame-length v*) arity+1) (seq (set *env* (sr-extend* sr v*)) (m+) ) (wrong "Incorrect arity") ) ) (make-closure the-function *env*) ) ) ) (dm meaning-fix-abstraction (n* e+ r t?) (let ((arity (len n*)) (r2 (r-extend* r n*)) (m+ (meaning-sequence e+ r2 #t)) ) (FIX-CLOSURE m+ arity) ) )

25. Calls (dm TR-REGULAR-CALL (m m*) (fun () (let ((f (m))) (invoke f (m*)) ) ) ) (dm REGULAR-CALL (m m*) (fun () (let ((f (m)) (v* (m*)) (sr *env*) (result (invoke f v*)) ) (set *env* sr) result ) ) ) (dm meaning-application (e e* r t?) (let ((m (meaning e r #f)) (m* (meaning* e* r (len e*) #f)) ) (if t? (TR-REGULAR-CALL m m*) (REGULAR-CALL m m*)) ) )

26. Argument Processing (dm meaning* (e* r size t?) (if (empty? e*) (meaning-no-argument r size t?) (meaning-some-arguments (head e*) (tail e*) r size t?))) (dm meaning-no-argument (r size t?) (ALLOCATE-FRAME size)) (dm meaning-some-arguments (e e* r size t?) (let ((m (meaning e r #f)) (m* (meaning* e* r size t?)) (rank (- size (+ (len e*) 1)))) (STORE-ARGUMENT m m* rank)))

27. Q: Where Do We Go From Here?

28. VM Introduction Portable Small Factors Implementation

29. VM Design Parameters Space –Runtime –Delivery Efficiency –Runtime –Compilation Analyzability –Inferred types –Control flow info

30. From Interpreter to VM Linearize Invent registers Invent stack Code generation through concatenation Byte code instructions

31. Byte Code Machine

32. “Instructions” (SHALLOW-ARGUMENT-REF j) (DEEP-ARGUMENT-REF I j) (DEEP-ARGUMENT-SET! I j m) (CHECKED-GLOBAL-REF I) (CONSTANT V) (SEQUENCE M M+) (FIX-LET M* M+) (CALL1 ADDRESS M1) (CALL3 ADDRESS M1 M2 M3) (REGULAR-CALL M M*) (ALLOCATE-FRAME SIZE) M,m1,m2,m3,m+,v are values Rank,arity,size,I,j are ints Address is predefined funct (PREDEFINED I) (SHALLOW-ARGUMENT-SET J M) (GLOBAL-REF I) (GLOBAL-SET! I M) (ALTERNATIVE M1 M2 M3) (TR-FIX-LET M* M+) (CALL0 ADDRESS) (CALL2 ADDRESS M1 M2) (FIX-CLOSURE M+ ARITY) (TR-REGULAR-CALL M M*) (STORE-ARGUMENT M M* RANK)

33. Introduce *val* Break implicit communication between instructions by introducing result register (dm CONSTANT (value) (fun () (set *val* value)))

34. Inventing Stack (dm STORE-ARGUMENT (m m* rank) (fun () (m) (let ((v *val*)) (m*) (set (frame-argument *val* rank) v)))) => (dm STORE-ARGUMENT (m m* rank) (fun () (m) (stack-push *val*) (m*) (let ((v (stack-pop))) (set (frame-argument *val* rank) v))))

35. Linearization (dm SHALLOW-ARGUMENT-SET! (j m) (fun () (m) (set (frame-argument! *env* j) *val*))) => (dm SHALLOW-ARGUMENT-SET! (j m) (cat m (SET-SHALLOW-ARGUMENT! J))) (dm SET-SHALLOW-ARGUMENT! (j) (lst (fun () (set (frame-argument! *env* j) *val*)))))

36. Inventing PC (dm REGULAR-CALL (m m*) (cat m (PUSH-VALUE) m* (POP-FUNCTION) (PRESERVE-ENV) (FUNCTION-INVOKE) (RESTORE-ENV))) (dm PUSH-VALUE () (lst (fun () (stack-push *val*)))) (dm POP-FUNCTION () (lst (fun () (set *fun* (stack-pop)))) (dm PRESERVE-ENV () (lst (fun () (stack-push *env*)))) (dm FUNCTION-INVOKE () (lst (fun () (invoke *fun*)))) (dm RESTORE-ENV () (lst (fun () (set *env* (stack-pop))))) (dm run () (let ((inst (head *pc*))) (set *pc* (tail *pc*)) (inst) (run)))

37. VM Dispatch Loop (dm run () (let ((instruction (head *pc*))) (set *pc* (tail *pc*)) (instruction) (run))) Already removed decode overhead by translating incoming bytecodes to direct calls Still extra overhead –Return from instruction –Loop

38. Reducing Dispatch Overhead Convert back to threaded code Each instruction is responsible for calling next one: (dm CONSTANT (value) (fun () (set *val* value) (let ((instruction (head *pc*))) (set *pc* (tail *pc*)) (instruction)) Example Push3 on PowerPC takes –2 instrs to do real push3 –11 total instrs for pc loop version –5 total instrs for threaded version Can do same with fast interpreter –Use CPS threading –Consing not an issue if closures created during pretreatment

39. Hand Selected Macro Instructions Still have overhead especially for simple instructions Can do better by combining oft-occurring sequences of instructions –amortizing the decode/dispatch overhead –customizing resulting instruction to remove any other interpretive overhead –shrinks byte-code size

40. Automatic Macro Instruction Detection Can run statistics off-line to determine best macros Can also run statistics on-line

41. VVM’s Tower of VM’s Record expansions Concatenate Peephole optimize at each level –Specialization of instructions Now that actual arguments are available –Between instruction optimizations

42. Boxing/Unboxing Objects all the way down Integers are objects and must be self-identifying One way to encode them is to wrap integer data in a “box” which also points to integer prototype Integer ops must then first unbox before running machine level integer op and finally box result We’ll be talking about alternative representations later

43. VVM Example (fun (x y) (* (+ x 1) y)) => (SHALLOW-ARGUMENT-REF 0 R0) (CONSTANT 1 R1) (I+ R0 R1 R2) (SHALLOW-ARGUMENT-REF 1 R3) (I* R2 R3 R4) (RETURN R4) (SHALLOW-ARGUMENT-REF 0 R0) (CONSTANT 1 R1) (UNBOX-INT R0 R2) (UNBOX-INT R1 R3) (PI+ R2 R3 R4) (BOX-INT R4 R5) (SHALLOW-ARGUMENT-REF 1 R6) (UNBOX-INT R5 R7) (UNBOX-INT R6 R8) (PI* R7 R8 R9) (BOX-INT R9 R10) (RETURN R9)

44. R/VVM’s Provides extensible VM infrastructure Permits the running of many different instruction sets customized to different languages Examples –Greg Sullivan’s DVM –Ian Piumarta’s VVM

45. Bytecodes can be Faster than Direct Execution When –There is a high price for cache misses –VM can fit inside cache –The interpretive overhead of the bytecodes is small (i.e., they are high-level) But –There will be cases where interpretive overhead is high Suggests hybrid approach –Demand driven translation –Analysis determines interpretive overhead

46. In C VM’s + Portable - Hard to interoperate with native code - Needs own versions of special forms - Needs its own FFI to C

47. In Language VM’s + Write VM in Same Language - Challenging to get high performance type system can be overly restrictive - Tough Bootstrap

48. IDVM In Dylan Virtual Machine was developed at Harlequin by Tony Mann and Eliot Miranda Consider IPVM

49. Example (dm is-numeric? ((x )) #t) => (dm IPVM-assign-constant-to-result ((vm-code ) (ip ) result (vars …)) (let ((new-result (elt vm-code ip)) (new-ip (+ ip 1)) (next-inst (elt vm-code new-ip))) (apply next-inst vm-code (+ new-ip 1) new-result vars))) (dm IPVM-return ((vm-code ) (ip ) result (vars …)) result) (fun ((x )) (let ((ip 0) (vm-code (vec IDVM-assign-constant-to-result #f IDVM-return)) (first-inst (elt vm-code ip))) (first-inst vm-code (+ ip 1) #f a)))

50. The Trick Considerable effort was expending optimizing the following idiom: (dm foo (a b c (thingies …)) … (apply somefunction a b c thingies)) Converted into a tail call with thingies being stack allocated Each IPVM instruction tail calls the next with code, pc, and locals as arguments.

51. Open Problems Compiler for Free with VVM’s Hybrid VM’s Type system’s powerful enough to do in language VM’s Analyzable VM

52. Readings Queinnec: Lisp In Small Pieces Deutsch, Schiffman: Efficient Implementation of the Smalltalk-80 System Piumarta: Optimizing threaded code by selected inlining Doyle, Moss: When are bytecodes faster than direct execution? Withington, McKay, Palter, Robertson: The symbolics virtual lisp machine or using the DEC alpha as a programmable micro-engine Mann, Miranda: A Dylan virtual machine in Dylan or son of Dylan Compiler in Dylan Folliot, Piumarta, Riccardi: A dynamically configurable multi-language execution platform Miranda: BrouHaHa - a portable smalltalk interpreter Ford et al: microkernels meet recursive virtual machines Chambers, Unger: Making pure object-oriented languages practical Ingalls: The Evolution of the Smalltalk Virtual Machine

53. Assignment #2 Write a meta circular interpreter for proto Implement all the special forms Assume (i.e., use) –the object system –the reader and parsing functions –the special forms themselves