Module 9: K-Normalization

Suggest the smart constructor for K.LET, does some unnesting (see let' in my code). Point to handout where let-floating is mentioned.

(This module is also available in PDF, in two-column and one-column formats.)


This week you’ll finally translate a real high-level language—and you’ll be able to run a vast quantity of Scheme code.

The module step by step

Before lab

Getting your UFT code ready for lab

  1. Get new code. Using git pull, get the new code for this module. I’m not expecting merge conflicts.

  2. Add a new pass to the UFT. You’ll integrate K‑normalization into the UFT by adding support for First-Order Scheme. This step is just like what you did in modules 5 and 6 to add support for K‑normal form and code generation.

    This is a key step. Some other pre-lab steps can perhaps be fudged, but if you skip this one, you’re unlikely to be able to use your lab time productively. The step looks like it has a lot of moving parts, but most of the parts are copy/paste/edit, so the step should go quickly.

    1. If you need to, review the handout on the UFT driver.

    2. Define reader function FO_of_file, which should work by composing the reader schemexOfFile with the projection function FOUtil.project. Its type should be

      val FO_of_file : instream -> FirstOrderScheme.def list error

      Function FO_of_file should look a lot like function KN_of_file.

    3. Define materializer function FO_of, which should materialize First-Order Scheme. If asked to materialize FO from FO, it should return FO_of_file; otherwise it should raise Backward (because as we’ll see next week, nothing else translates into FO).

    4. Define emitter function emitFO by composing emitScheme with embedding function FOUtil.embed. Function emitFO should look a lot like function emitKN.

    5. Add a case to translate to handle the case when outLang is FO. It should look a lot like the other cases.

    6. Update the function KN_reg_of that you wrote in module 6. Your current version should have a catchall case that raises NoTranslationTo KN. Replace that case with a function that materializes FO and composes the result with KNormalize.def. The materializer can fail, but K‑normalization cannot fail, so you will have to manage the error type. If you need help, consult the handout on composing functions with error types.

    7. Update the KN_text_of function you wrote in module 5. The updated function should either read K-normal form from a file or should return a result from KN_reg_of, with registers renamed to strings. A suitable renaming will rename register 1 to string "r1", and so on.

Getting your brain ready for lab

  1. Read about K‑normalization and register allocation. Read and understand the background handout K-Normalization and Register Allocation. Only two sections are important for lab: the introduction and the section “Our approach to register allocation.” You can fudge the rest, but if you don’t grok those sections, you may have a hard time being productive in lab.

  2. Review the source and target languages. Review the abstract syntax of first-order vScheme in file foscheme.sml. This language is a subset of the Unambiguous vScheme you worked with in module 5.

    Review the abstract syntax of your K‑normal form in file knf.sml, also from module 5.

    You can fudge this step.

  3. Look over the environment interface. In file env.sml you’ll find an environment interface. It’s the same one used for disambiguation in module 5, but in module 5 you didn’t have to pay much attention. In your K‑normalizer you’ll need not only to look up names in an environment but also to build new environments.

    The only environment you’ll use in lab is the empty environment Env.empty, so in practice you can postpone this step until after lab.


In lab you will start building the following ML module, which is templated for you in file knormalize.sml:

structure KNormalize :> sig
  type reg = int  (* register *)
  type regset     (* set of registers *)
  val regname : reg -> string
  val exp : reg Env.env -> regset -> FirstOrderScheme.exp -> reg KNormalForm.exp
  val def :                          FirstOrderScheme.def -> reg KNormalForm.exp

The FirstOrderScheme structure is defined in source file foscheme.sml; it is just like Disambiguated vScheme, except it doesn’t have LETREC or LAMBDA. The KNormalForm structure is the one you built in module 5.

Register sets

  1. Limited implementation of register sets. You can do a full-blown implementation of sets, but because the K‑normalizer always picks the smallest available register, I recommend a trick: represent only sets of contiguous available registers. For example,

    datatype regset = RS of int  (* RS n = represents { r | r >= n } *)

    A set of registers needs to support two main operations:

    val smallest : regset -> reg
    val -- : regset * reg -> regset   (* remove a register *)

Allocating registers in continuation-passing style

  1. Allocating registers with smart let bindings. The primary operation performed by the K‑normalizer is “put the value of this expression in a register.” This operation will be implemented by function bindAnyReg, which ensures that a target-language expression (type exp) is in a register—allocating that register if need by. Function bindAnyReg uses continuation-passing style; the continuation has type reg -> exp.

    type exp = reg KNormal.exp
    val bindAnyReg : regset -> exp -> (reg -> exp) -> exp

    As described in the background handout, bindAnyReg has this contract: bindAnyReg A e k returns an expression e’ that is roughly equivalent to

    let t = e in $(k t)⟧, where t ∈ A

    In detail,

    • Evaluating e’ may kill registers in A (and only in A)

    • The result of evaluating e’ is equivalent to the result of evaluating let t = e in e’’ where t is an element of set A and expression e’’ is produced by applying continuation k to t.

    • If e has the form of a name y, then e’ equals k y. That is, if e is already in a register, bindAnyReg reuses that register; it does not allocate a new one.

    Less precisely, bindAnyReg binds the value of e into a newly allocated register, unless e is in a register already. It then continues with the identity of the register that holds e.

    Function bindAnyReg need not manage the set of available registers. That will be the job of the calling function, exp.

    Implement bindAnyReg.

K-normalizing a primitive call

  1. Special-case primitives. Implement enough cases so that you can K‑normalize a call to print, like this:

    $ echo "(println 'first-steps)" | uft fo-kn
    (let ([$r0 'first-steps]) (println $r0))

    You’ll need these cases:

    • special case in exp that handles F.PRIMCALL (p, [e])
    • A case in exp that handles F.LITERAL v
    • A case in def that handles F.EXP e

    The source-language expression F.PRIMCALL (p, [e]) should be K‑normalized by recursively K‑normalizing e, binding the result to a register, and generating a primitive call in K‑normal form. (A very similar example can be found in a handout.) In step 18 you will generalize this case to handle multiple arguments to a primitive.

    The F.LITERAL v case you can figure out.

    The F.EXP e case means an expression at top level, so that means K‑normalizing e in a context where all registers are available and the environment is the empty environment Env.empty.

After lab

Be alert to let*

You might be surprised by seeing let* in your debugging output, when you haven’t done anything to support it. As always, let* is syntactic sugar for a nested series of let expressions. My parser desugars let* using function letstar in file vscheme-parse.sml. And my prettprinter resugars nested let forms into let* , using function nestedBindings in file wppscheme.sml. So internally there are only let and letrec; let* is only syntactic sugar.

The desugaring and resugaring enables us to work with more readable code.

Unit tests

Next you’ll build unit tests. They make testing everything else easier.

  1. Unit tests. Your source language is a subset of Unambiguous vScheme, and the F.CHECK_EXPECT form includes strings representing both expressions. To K‑normalize F.CHECK_EXPECT, all you need to do is generate a sequence of the form (e₁; e₂) where e₁ calls primitive check and e₂ calls primitive expect. Each primitive call will have the form @(t, v), where @ is P.check or P.expect, t is a register holding the value of an expression and v is the literal string describing the source code of the expression. Because such a call is a primitive call taking exactly one register, the code will closely resemble your K‑normalization of print in step 8. You may want to write a helper function.

    The F.CHECK_ASSERT form is similar.

    In your def function, write cases that K‑normalize the F.CHECK_EXPECT and F.CHECK_ASSERT forms. You will use primitives P.check, P.expect, and P.check_assert. And since these forms are executed at top level, all registers are available. You’ll test this code in the next step.

  2. Start a test file. Test your work so far by creating file kntest.scm. This file should contain first-order Scheme that your system can successfully K‑normalize, generate code for, assemble, and run. Start with these three tests:

    (check-expect (number? 3) #t)            
    (check-expect (number? 'really?) #f)
    (check-assert (symbol? 'really?))

    Confirm that everything works:

    > uft fo-vo kntest.scm | svm
    All 3 tests passed.

Easy cases

First-Order Scheme has a bunch of forms that neither allocate registers nor manipulate environments. These forms are relatively easy to K‑normalize—every subexpression uses the same set of available registers as its parent expression

  1. Local variables. Using the environment passed to exp as a parameter, K‑normalize the F.LOCAL and F.SETLOCAL forms. Neither of these forms has to allocate a register.1

    You won’t be able to test local variables until you can create some local variables—for example, using let or lambda. But once you’ve done step 20, come back and add test cases to kntest.scm, and document them with comments ;; F.LOCAL and ;; F.SETLOCAL.

  2. Global variables. Using the environment passed to exp as a parameter, K‑normalize the F.GLOBAL and F.SETGLOBAL forms.

    • The F.GLOBAL form translates into a call to primitive function P.getglobal.
    • The F.SETGLOBAL form, like print, takes a parameter that has to be allocated into a register. And its translation is nuanced: in Scheme source code, F.SETGLOBAL is executed both for value and for side effect. But in K-normal form, primitive P.setglobal is executed only for side effect. So the F.SETGLOBAL form has to be translated into a let expression whose body has the form (setglobal(localname, globalname); localname).

    K-normalize these two forms. Add test cases to kntest.scm and document them with comments like those in step 11.

    Now K-normalize the F.VAL definition form. It desugars into a F.SETGLOBAL.

  3. Sequence. The Scheme begin form sequences the evaluation of a list of expressions: none, one, or many. No registers have to be allocated or preserved. An empty begin must evaluate to #f; a nonempty begin must evaluate to the result of its last expression. To implement this semantics, it will be useful to define a recursive helper function.

    K‑normalize form F.BEGIN, add test cases to kntest.scm, and document the test cases.

  4. Control flow. K‑normalize the F.IFX and F.WHILEX forms. Both forms include a condition that must be stored in a register.

    • The F.IFX form is K‑normalized by evaluating the condition, binding the result to a temporary register t, and using t in the K‑normal form if. This transformation works because the condition is evaluated only once.

      Also, once the condition has been evaluated, the decision is made, so register t is dead. That means register t is available to be reused in both branches.

    • The F.WHILEX form is K‑normalized by allocating an available register t that does not bind the result of the expression. Instead that register is used in the K‑normal form as in while t := e do e’, where the condition is evaluated multiple times. Register t is assigned to on every trip through the while loop.

      As in the if, register t is available to be used in both e and e’.

    K‑normalize F.IFX and F.WHILEX, add test cases to kntest.scm, and document the test cases.

Environment manipulation: Function definitions

The define form K‑normalizes into an assignment to a global variable. The right-hand side of the assignment is FUNCODE, and the key is to get the right names and environments:

To set the global variable, you need to put the FUNCODE in a register. At top level, register 0 is always available.

  1. Function definition. In your def function, K‑normalize the F.DEFINE form.

    Add a simple test case to your kntest.scm and document it. (Until you can call the function, about all you can do is apply a type predicate like function?.)

Expressions that allocate multiple registers: Calls

Calls are the first form for which your K-normalizer has to manage the set of available registers. For example, if you are K-normalizing (append e₁ e₂), the register that holds the normal form of e₁ is not available while e₂ is being normalized. And the computation of the available-register set depends on the form of the call: A primitive call may use any available registers; a function call must use consecutive available registers. Each form therefore requires a different register-allocation policy. But they are otherwise K-normalized using the same algorithm. You will implement this algorithm using a higher-order function that takes the register-allocation policy as a parameter.

The core of the algorithm, which implements both calls, is to bind a list of actual parameters to a nest of let-bound names. Compared with bindAnyReg, there are two complications:

  1. Warmup: Normalize a primitive with two arguments. Add another special case in exp that handles F.PRIMCALL (p, [e1,e2]). This case should create a continuation that removes a temporary register from the available set.

    Test your code as follows:

    $ echo "(println (car (cons 'second-step '())))" | uft fo-vo | svm

There are too many cases to deal with them all by hand. Instead, you will define a higher-order function that handles every first-order form that takes multiple arguments. This function will be parameterized by a register-allocation policy. The type of a policy is the same as the type of bindAnyReg:

type policy = regset -> exp -> (reg -> exp) -> exp

A policy looks at the exp and regset and chooses a reg to be passed to the continuation (which has type reg -> exp).

You will define a function nbRegsWith, which takes a normalizer and a policy and returns a register-allocation function for lists of items.

type 'a normalizer = regset -> 'a -> exp
val nbRegsWith : 'a normalizer -> policy ->
                 regset -> 'a list -> (reg list -> exp) -> exp

The name is short for “K‑normalize and bind to registers.” With the help of a suitable parameter of type ’a normalizer, this function can K-normalize any list, but in this module, you will use it only to normalize lists of first-order Scheme expressions.

By contract, nbRegsWith normalize p A es k returns a nested let expression that

You will implement nbRegsWith and use it to K‑normalize both forms of call.

  1. Implement function nbRegsWith. Define function nbRegsWith. The key ideas are as follows:

    • Function nbRegsWith is an ordinary recursive function that consumes a list, and it deals with just two cases: the input list is either [] or e::es.

    • When the input list is [], the result is just k [].

    • When the input list is e::es, nbRegsWith must K‑normalize e, bind the result to a register according to policy p, then continue by recursively binding the remaining es. The binding to e may use any of the available registers in A. The recursive bindings to es may also use any of those available registers except the register to which e is bound.

      Each of these bindings requires a continuation: the call to p needs a continuation that expects the single temporary to which e is bound. And the recursive call needs a continuation that expects the list or temporaries to which the remaining es are bound.

    The key to getting the code right is constructing the right continuations to pass to both the policy and the recursive call. These are new continuations that you will have to synthesize using fn. (You might wish to review the synthesis of new continuations in the Boolean-formula solver from CS 105.) If your continuations have the right types and they remove the register that binds e, you are probably in good shape.

    List processing in continuation-passing style: An example

    If your continuation skills are rusty, check out this example where I transform an ordinary recursive map into a CPS map'. The standard (“direct style”) map looks like this:

    val map : ('a -> 'b) -> 'a list -> 'b list
    fun map f = []
      | map f (x :: xs) =
          let val y  = f x
              val ys = map f xs
          in  y :: ys

    The intermediate results are let-bound to names y and ys for this reason: in the continuation-passing version, those names become lambda-bound names (parameters to a continuation written with fn). And the function parameter is also in continuation-passing style:

    val map' :  ('a -> ('b -> 'answer) -> 'answer)
             -> 'a list
             -> ('b list -> 'answer)
             -> 'answer
    fun map' f' [] k = k []
      | map' f' (x :: xs) k =
          f' x (fn y => map' f' xs (fn ys => k (y :: ys)))

    Function map' synthesizes two continuations: one passed to f' which receives y, and one passed to the recursive call, which receives ys. These two continuations cooperate to build the list y :: ys which is then passed to the original continuation k.

    Your function nbRegsWith will be structured along similar lines. In the role of function f', you will provide a policy, and the 'answer type will be exp. And you will synthesize two continuations: one that takes a single temporary register, and one that takes the rest of the temporary registers.

    Confirm that nbRegsWith has the right type by placing the following lines after the definition of nbRegsWith:

    val nbRegsWith : 'a normalizer -> policy -> regset -> 'a list -> (reg list -> exp) -> exp
      = nbRegsWith
  2. Implement primitives. Retire your special-case primitive code from step 8. Replace it with general-case code that K‑normalizes F.PRIMCALL (p, es), where list es may contain any number of expressions.

    The template I provide you for the exp function includes an internal definition of nbRegs, which normalizes and binds expressions in the current environment rho. Your case for F.PRIMCALL should use nbRegsWith with bindAnyReg as the policy.

    Add suitable test cases to kntest.scm and document them.

  3. Define a consecutive-register policy. Define a function

    val bindSmallest : regset -> exp -> (reg -> exp) -> exp

    that behaves just like bindAnyReg, except it doesn’t optimize for the case of an expression already in a register. That is, bindSmallest e A k returns let t = e in e’’ where t is the smallest register in set A and expression e’’ is k t. When used sequentially, bindSmallest will produce consecutive registers.

    If you like, you can refactor bindAnyReg so that it and bindSmallest share some code.

  4. Implement function calls. K‑normalize the F.FUNCALL form. You will need to use bindSmallest to put the function in the smallest available register, then K‑normalize the arguments using nbRegs with bindSmallest as the policy. When K-normalizing the arguments, do not overwrite the register that holds the function.

    Like the helper function nbRegsWith itself, this one is all about finding the right continuations.

  5. Test function calls. Add simple test cases to kntest.scm and document them.

    • Tests for function calls and function definitions
    • Tests for local variables that you put off in step 11

    For a more ambitious test, try un-nesting calls, as in this example from a functional Quicksort:

    (append (qsort (filter left? rest))
            (cons pivot (qsort (filter right? rest))))

    K-normalizes to

    (let* ([$r0 append]
           [$r1 qsort]
             [$r2 filter]
             [$r3 left?]
             [$r4 rest]
             [$r2 ($r2 $r3 $r4)]
           [$r1 ($r1 $r2)]
           [$r2 pivot]
             [$r3 qsort]
               [$r4 filter]
               [$r5 right?]
               [$r6 rest]
               [$r4 ($r4 $r5 $r6)]
             [$r3 ($r3 $r4)]
           [$r2 (cons $r2 $r3)]) 
      ($r0 $r1 $r2))

Expressions that allocate multiple registers: Scheme’s let

Like a call, a let binds a sequence of expressions to a list of registers. What’s interesting is the K‑normalization of the body: The body needs to be normalized in an environment that knows in what register each let-bound name is placed. And in the body, none of those registers are available.

  1. Review the difference between let and let*. The difference is entirely in the environments. In a let form, no bound names are visible on any right-hand side. All right-hand sides are evaluated in the same environment. In a let* form, names bound in earlier bindings are visible on the right-hand sides of later bindings. Every right-hand side is evaluated in a different environment.

    Optional: Grab the μScheme interpreter from Chapter 5 of Programming Languages: Build, Prove, and Compare (page 310), and compare how environments are manipulated in the interpretation of let and let*.

    Create test cases in first-order Scheme that shows the difference between let and let* with the same bindings. Be sure that at least one test case includes something of the form (let ([x y] [y x]) …), where both x and y are local names. (Function parameters will do.) Confirm your understanding by making sure your tests pass the vscheme interpreter.

  2. Implement let bindings. K‑normalize the F.LET form. I encourage you to use nbRegs; you can figure out an appropriate policy. The continuation will have to remove all the bound registers from the available set. You can implement this operation in a special-purpose recursive function, or you can use a fold with a “flipped” version of function --.2

    Some functions in the [ListPair].meta interface are useful here.

    Run your code on the let binding from the test case you wrote in step 22. If you choose the bindAnyReg policy and if both x and y are already in registers, the swap will generate no code at all. Your UFT will simply K‑normalize the body of the let using a different environment in which the names are swapped. With the bindAnyReg policy, that’s expected behavior, not a bug. If you have concerns about that behavior, choose a different policy.

    Add your tests to kntest.scm. Document them.

Integration test

  1. Predefined functions. Use the vscheme interpreter to extract the first-order predefined functions:

    vscheme -predef | grep -vw lambda > fopredef.scm

    Confirm that your system can compile and load all these functions:

    uft fo-vo fopredef.scm | svm

    There should be no output: no test results, no complaints.

    If so, place your compiled functions in the build directory by running

    make predef

    from the vscheme directory.

  2. End-to-end testing. Demonstrate the complete viability of your system via end-to-end testing:

    • If you are now taking or have taken CS 105 at Tufts, create a file scheme105.scm that contains your complete solution to the CS 105 scheme homework (the third homework) of whatever version you took. Update the source code as follows:

      • If any check-error form takes more than one line, remove it.

      • If any definitions or tests depend on lambda (likely if you did arg-max), remove those also.

      Confirm that all the check-expect and check-assert tests pass with both the uscheme and vscheme interpreters.

    • If you have not taken CS 105 at Tufts, implement a recursive merge sort on a list of integers, not higher-order. Write test cases. Put your results in a file msort.scm. Confirm that the tests pass with vscheme.

    Confirm that your stuff compiles with your UFT and that all tests pass with the SVM. In your bin directory I have provided a shell script that compiles and runs first-order code with the first-order predefined functions:3

    $ run-fo-with-predef scheme105.scm

    If anything goes wrong, take notes, which you will use in a learning outcome.

  3. Congratulate yourself. Congratulations! You have implemented a complete (if small) first-order language that can run efficiently on commodity hardware. We won’t stop now—we’ll be doing higher-order functions next week—but this is a good time to recognize just how far you’ve come.

What and how to submit

  1. On Monday, submit the homework. In the src/uft directory you’ll find a file SUBMIT.09. That file needs to be edited to answer the same questions you answer every week.

    To submit, you’ll need to copy your working tree to the department servers. We recommend using rsync, but scp also works.

    Now log into a department server, change to your working tree, and submit your entire src directory:

    provide cs106 hw09 src

    or if you keep an additional tests directory,

    provide cs106 hw09 src tests
  2. On Tuesday, submit your reflection. Create a plain text file REFLECTION, which will hold your claims for project points and depth points.

    For each project point you claim, write the number of the point, plus whatever is called for in the section “How to claim the project points”—usually a few sentences.

    Now copy your REFLECTION file to a department server and submit it using provide:

    provide cs106 reflection09 REFLECTION

Overview of the code

Code you will write or edit


The UFT driver, which you have edited before. You’ll be adding an new language and translation, just as you did in modules 5 and 6.


You’ll define your K‑normalizer here.

Code you will need to understand


A definition of environments. Your find calls should always succeed, so you should not catch the NotFound exception—if that exception is raised, it indicates a bug in your UFT.


Defines First-Order Scheme, which is a subset of Unambiguous vScheme. The ASTs are identical, except that First-Order Scheme lacks lambda and letrec.

New code that you don’t need to care about


Embedding and projection for First-Order Scheme. This is the easiest embedding/projection pair ever. Because the code is boring and repetitive, I’ve written it for you.

Learning outcomes

Outcomes available for points

Learning outcomes available for project points:

  1. Craft. You can add a new pass to the UFT driver.

  2. Continuations and register allocation. You can define a continuation that reserves an allocated register, preventing its reuse.

  3. Continuations and register reuse. You can define a continuation that reuses an allocated register.

  4. Functional programming. You can use higher-order functions to avoid near-duplicate code.

  5. Calling conventions. You can implement a procedure calling convention.

  6. New calling conventions. You can identify implications of changing calling conventions.

  7. Syntactic-form testing. Your K‑normalizer is comprehensively tested.

  8. Integration testing. Your K‑normalizer passes an integration test.

  9. Functional languages and mutation. You can explain how the possibility of mutation affects choices available to the UFT.

Learning outcomes available for depth points:

  1. Let-floating [3 points]. Use the equations of translation (the 𝓡 function) from module 6 to show when the following two expressions have the same translation:

    • let y = (let x = e₁ in e₂) in e₃
    • let x = e₁ in (let y = e₂ in e₃)

    Hint: it works when x is not free in e₃ and in one other special case.

    Then deploy your insight in your K‑normalizer to rewrite the first form to the second form. You’ll know you’ve got it right when your uft fo-kn produces no nested let expressions—my prettyprinter will sugar everything into let*.

    Hint: define a so-called “smart constructor” to use in place of K.LET.

  2. More powerful if instructions [3 points]. Extend your SVM to include two-register if instructions that can compute a Boolean expression and “skip next if false”, all in a single VM instruction. For example, if r1 < r2. Similarly, extend your SVM to include a one-register if instruction that uses the type predicate null? in the condition.4 Extend your K‑normal form and your K‑normalizer to exploit these instructions. Demonstrate your extensions and measure how much the extension can shrink your generated VM code.

  3. Small-integer literals [3 points]. Extend your SVM to include instructions in R2U8 or R2I8 format, so that expressions like x > 0 or n + 1 can be computed using a single VM instruction. Extend your K‑normalizer to exploit these instructions. Demonstrate your extensions and measure how much the extension can shrink your generated VM code.

  4. Optimized let expressions [2 points]. K‑normalize let by defining a hybrid policy that results in code that is superior to whatever you would get by using a one-size-fits-all policy in step 23.

  5. Efficient compilation of long list literals [1 point]. Using strict left-to-right evaluation for long list literals uses a number of registers proportional to the length of a list. That means, for example, that we can’t compile a literal list with 15 numbers, because we would run out of registers. But if the first argument to cons is a literal value, we can change the evaluation order to compute the second element first. Do so, and confirm that your UFT can compile a literal list of numbers, no matter how long, using only two registers.

  6. Polymorphic code generation [1 point]. Assuming you’ve already implemented A-normal form, define your K-normalizer as a functor so that your K-normalizer can generate both K-normal form and A-normal form, just by applying the functor to two different actual parameters.

  7. Bignums in vScheme [2 points]. (Not related to K-normalization.) Repurpose your SML bignum implementation from CS 105 to work inside the vScheme interpreter. Add a suitable type of value, and update the arithmetic primitives so they do mixed arithmetic. (Alter higher-order function arithOp to handle mixed inputs and to provide a single point of truth about promotion rules.)

  8. Bignums in the SVM [4 points]. (Not related to K-normalization.) Implement bignum arithmetic in the SVM. You can port one of your implementations to C, or you can port my array-based implementation from μSmalltalk. Or you can try using the GNU multiprecision library (gmp).5

How to claim the project points

  1. To claim this point, submit source code that compiles and builds a uft binary that understands what fo-kn is asking for.

  2. To claim this point, identify a line of your code that contains a continuation passed to bindAnyReg, bindSmallest, or a similar function, and explain how the continuation reserves the allocated register to prevent its reuse.

  3. To claim this point, identify a line of your code that contains a continuation passed to bindAnyReg, bindSmallest, or a similar function, and explain how the continuation does not reserve the allocated register but rather allows its immediate reuse.

  4. To claim this point, identify (by line number) every case in your K‑normalizer that binds a list of expressions to a list of registers, and confirm that each case uses the same higher-order function.

  5. To claim this point, identify the lines of your code where it is determined that a function’s incoming actual parameters are in consecutive registers starting at register 1. THIS QUESTION IS ALMOST UNIVERSALLY FAILED. CONSIDER REWORDING Idea: To claim this point, identify the lines of your code where it is determined that a function expects to receive its incoming actual parameters in consecutive registers starting at register 1.

  6. Suppose we change the calling convention so that the function register and argument registers are not killed by a call. Instead they are required to have the same values after the return that they had at the call. With this change, it becomes very difficult to use the tailcall instruction except for direct recursion. And other changes, unrelated to tail calls, might also be required in the UFT. To claim this point, identify one such required change, either in the K-normalizer or in the code generator.

  7. To claim this point, submit a file kntest.scm in which every value constructor in foscheme.sml, for both exp and def types, is exercised by some test. Each test must be documented by a comment that names the value constructor or value constructors that it tests. And uft fo-kn kntest.scm must actually generate code. (It is not necessary for the generated code to run or for all the tests to pass.)

  8. To earn this point, your system must run and pass all the tests in scheme105.scm. To claim the point, let us know that you accomplished this goal, and in the reflection, tell us how many tests are included in the file. If anything went wrong in your first run of step 25, let us know one thing that went wrong and how you fixed it.

  9. To claim this point, justify your choice of policy for K‑normalizing a let expression in step 23. Justification should include an explanation of why another policy is inferior and should be demonstrated with a code example.

    Keep in mind that vScheme local variables are mutable, and mutating a local variable \(x\) must not change the value of a distinct variable \(y\). That’s; that’s what makes this issue difficult.

  1. The disambiguator guarantees that every local variable is defined. It is up to the K‑normalizer to build the environment correctly so that the lookup always succeeds. If your environment lookup fails, either there is a bug in your disambiguator or there is a bug in your K‑normalizer.

  2. You’ll have to define flip.

  3. This shell script has some sanity checks. For example, if your compiled predefined functions are older than your UFT, it complains.

  4. Other type predicates optional.

  5. Field reports suggest that gmp is a mixed blessing. At best.