1 CS 312 – Lecture 28 Continuations –Probably the most confusing thing you’ve seen all semester… Course summary –Life after CS 312

2 Continuations SML/NJ has ability to capture a control context as a value: a continuation Continuation = “the rest of the program” Example: if x<0 then ~x else x fn(z:bool) => if z then ~x else x fn(a: int)=>a fn(a: int)=>~a

3 Continuations SML/NJ has ability to capture a control context as a value: a continuation Continuation = “the rest of the program” Example: if x<0 then ~x else x So we can rewrite the above code as (fn(z:bool) => if z then ~x else x) (x < 0) fn(z:bool) => if z then ~x else x

4 Continuations in ML In CPS, functions don’t return – they just pass their value to another function. Open SMLofNJ.Cont: ‘a cont is a continuation expecting a value of type ‘a throw: ‘a cont -> ‘a -> ‘b throws control to continuation, never comes back throw c v means: “You are c. Here is value v for you, take it and take the control flow too; I’ve done my job and I will disappear, so don’t come back to me with your result.”

5 Continuations in ML callcc : (‘a cont->`a)->`a callcc f invokes f passing it the current continuation callcc f means: “I will now pass control to f, so that f can perform some computation." throw = sending a value to a continuation callcc = sending a continuation to a function When f is done, it will send something of type `a to the continuation it received. directly, using a throw, or indirectly, with another callcc So, the continuation must have type `a cont and f will have type (`a cont -> `a). In the end, it will be as though the callcc f had returned `a.

6 Example if (x < 0) then ~x else x One way to write it in Continuation Passing Style: let fun f (x: int) (c: int cont): int = throw c (~x) fun g (x: int) (c: int cont): int = throw c (x) fun t (y: bool*int as (z, x)) (c: int cont): int = if z then callcc (f x) else callcc (g x) fun h (x: int) (c: int cont): int = callcc ( t ((x<0), x)) in callcc (h ~10 ) end

7 Handling errors Can be used in place of exceptions to send control to an arbitrary place let fun g(n: real) (errors: int option cont) : int option = if n < 0.0 then throw errors NONE else SOME(Real.trunc(Math.sqrt(n))) fun f (x:int) (y:int) (errors: int option cont): int option = if y = 0 then throw errors NONE else SOME(x div y+valOf(g 10.0 errors)) in case callcc(f 13 3) of NONE => "runtime error" | SOME(z) => "Answer is "^Int.toString(z) end

8 First-class continuations Can store continuations for future use! let val cref: int cont option ref = ref NONE fun f(c: int cont): int = (cref := SOME(c); 5) in callcc f; case !cref of NONE => "finished" | SOME(co) => throw co 4 end

9 Continuations - summary Control context is encoded as a value and passed around in the program Can be used to transfer control flow to arbitrary points in the program –related feature 1: gotos in low-level code –related feature 2: setjmp/longjmp in C –useful for exceptions –useful in compilers and interpreters "You need to learn continuations about three times before they really start making sense."

10 What happened in CS 312? Design and specification of programs –modules and interfaces –documenting functions and ADTs –programming in functional style –testing Data structures and algorithms –collections –graphs –showing correctness and complexity Programming languages –Features and methodologies –models of evaluation –implementation

11 Life after SML 312 is not about SML or even about functional programming Lessons apply to Java, C, C++, etc.

12 Design Break up your program into modules with clearly defined interfaces (signatures) Use abstract data types (data abstractions) Good interfaces are narrow, implementable, but adequate Avoid stateful abstractions, imperative operations unless compelling justification Testing strategy and test cases: coverage

13 Specification Good specifications: clear, simple, concise, accurate Think about your audience Avoid over-specification Abstraction barrier: user should not need to know implementation/representation Convince someone that every spec is met Specify representation invariants and abstraction functions

14 Data structures and algorithms Collections (ordered and unordered) –Sets, maps –Lists, arrays –Hash tables –Binary search trees (red-black, splay, B-trees) –Priority queues/heaps Graphs –BFS, DFS, Dijkstra CS 482 and 472

15 Data structures and algorithms String and regular-expression matching –CS 381/481 Mutable vs. immutable data structures Locality –Everywhere (theoretical and applied courses)

16 Correctness and complexity Using specifications, invariants to reason about correctness Constructing, solving recurrence relations Worst-case run time, average case run time, amortized run time Proofs by induction CS 482

17 Programming languages Features –Higher-order functions –Explicit refs –Recursive types and functions –Lazy vs. eager evaluation –Concurrency Evaluation models (semantics) –Substitution –Environments and closures Implementation –Type checking and type inference –Memory management, garbage collection CS 411, 412