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Stephanie Weirich Cornell University. Resource Bound Certification. Joint work with Karl Crary, CMU. Problem. Sometimes code runs too long How do we prevent it?. Enforcement Methods. Run the code, and if it takes too long, kill it Look at the code and decide how long it will take

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stephanie weirich cornell university
Stephanie Weirich

Cornell University

Resource Bound Certification

Joint work with Karl Crary, CMU

problem
Problem
  • Sometimes code runs too long
  • How do we prevent it?
enforcement methods
Enforcement Methods
  • Run the code, and if it takes too long, kill it
  • Look at the code and decide how long it will take
    • Annotations on the code can makes this process decidable
dynamic method
Dynamic Method
  • Can decide how long is too long on the fly (even while the code is running)
  • Code producer does not have to write code in any particular language (or include annotations)
  • May be expensive to enforce
static method
Static Method
  • Guarantee to the user that code will run without being prematurely terminated
  • May provide faster execution
  • Can encompass dynamic checking
    • Static verification that the code executes dynamic checks
    • Makes policy enforcement part of the model
annotations
Annotations
  • What annotations can we use for execution time verification?
base language
Base Language
  • Type-Safe version of C
    • all pointer accesses checked for NULL
    • no pointer arithmetic
    • safe memory-management (garbage-collection or region-based)
    • tagged unions
caveat
Caveat
  • Most examples in this talk will look like functional programming
  • Intension is to verify a low-level language (such as type-safe assembly language)
    • Don’t have to trust the compiler
    • Hopefully annotation methodologies are general enough that people can be clever
certification of running time
Certification of Running Time
  • Simplification: Only count function calls and loop iterations
  • Functions and loops annotated with the cost of execution

int add1(int x)<0> {

return x+1;

}

int add3(int x)<3> {

x = add1(x); x = add1(x);

return add1(x);

}

example
Example
  • Function-typed arguments can influence the running time

int foo(int f(int x)<3>)<8> {

return f(1)*f(3);

}

  • But can restrict how the code is used

foo(add1);

foo(add3);

abstract time
Abstract Time
  • Useful to abstract the time annotation

int foo (int f(int x)<k>) <2k+2> {

return f(1) * f(3);

}

foo (add1); // k=0, takes 2 steps + 1 for call

foo (add3); // k=3, takes 8 steps + 1 for call

static dependent cost
Static Dependent Cost
  • What if the time depends on a non-function argument?
  • Essential for recursive functions

uint fact (uint n)<n>{

if (n == 0) return 1;

else return (fact (n-1) * n);

}

  • Note :
    • Time annotation must be non-negative
    • Ignore underflow/overflow
slide13
Size
  • What if the argument is not a uint or a function?
  • Option: Pre-defined mapping from structured data values to their “sizes”
example list
Example - List

struct list {

const uint val;

const struct list* next;

}

  • The size of a list is its length
  • Simplifying assumption - The members of the struct are const so that we do not have to track aliasing.
slide15
Sumf

int sumf (uint f(uint)<k>; struct list* x; int acc) <length(x)*(k+2)> {

if (x == NULL) return acc;

else {

int acc2 = acc + f (x->val);

struct list* x2 = x -> next;

sumf (f, x2, acc2);

}

}

calling sumf
Calling Sumf

sumf(add3, NULL,0); // 0*(3+2) + 1

struct list* x = new { val = 5; next = NULL };

sumf(add3, x,0) // 1*(3+2) + 1

slide17
Size
  • What if the time of f is dependent?

uint sumf (uint f(uint y)<y>;

struct list* x; uint acc) <length(x)*(??+2)> {

if (x == null) return acc;

else {

uint acc2 = acc + f (x->val);

struct list* x2 = x -> next;

sumf (f, x2, acc2);

}

}

user defined size
User-defined size
  • Need a programming language to express the mapping between datatypes and the time to iterate over them
    • Expressive enough to represent structured data, and functions over that data
    • Not so expressive that equivalence is undecidable
  • Need a way to connect dynamic data with a representation in this language
decidable expressive language
Decidable, Expressive Language
  • Typed-lambda calculus with products, sums and primitive recursion over inductive types
  • Syntax of functional programming language ML
  • Terminology
    • Dynamic language -- Type-safe C
    • Static language -- this annotation language
static language
Static Language
  • Natural numbers (of type nat) and arithmetic operations
    • 3+4 , 5*x
  • Higher-order functions
    • fn (x : nat) => (fn (y :nat) => x+y)
    • (of type nat  (nat  nat) )
  • Tuples
    • (3,5) : nat  nat
static language1
Static Language
  • Sums and recursive types notated with datatypes

datatype bool = False | True

fun not (b:bool) = case b of

True => False

| False => True

primitive recursion
Primitive Recursion
  • datatypes can recursively mention name only in positive positions

 datatype foo = Bar of foo

| Baz of foo * foo

datatype foo = Bar of foo  foo

 datatype foo = Bar of (foo  int)  foo

primitive recursion1
Primitive Recursion
  • Recursive functions over these datatypes can only call themselves on subterms of their arguments

datatype foo = Bar of foo

| Baz of foo * foo

fun iter (x : foo) = case x of

Bar(w) => iter(x)

| Baz(y,z) => iter(y)

list representation
List Representation

datatype list = Null | Cons of nat * list

fun time(m : list) =

case m of

Nil => 0

| Cons(val, next) =>

val+2+time(next)

decision procedure
Decision Procedure
  • We must be able to decide if two terms in the static language are equivalent
  • Algorithm: convert each term to a normal form and compare
  • Need a reduction system for terms that is confluent and strongly normalizing
reduction rules
Reduction Rules
  • Sample Reduction rules
    • 3 + 4 --> 7
    • M + 0 --> M
    • case Ci M of

C1 x1 => N1

| C2 x2 => N2 --> Ni [M/xi]

    • (fun f x => M ) N -->

M[N/x, (fun f x => M)/f]

connecting the languages
Connecting the Languages
  • We must be able to use this static language to describe the values of the dynamic language
  • Use the type system to enforce that a dynamic term matches a static description
singleton types
Singleton Types
  • nat represents unsigned ints
  • Connect constants in the two languages
  • If m : nat , form singleton type uint<m>

uint<3> x;

 x = 3;

 x = 4;

using fact
Using fact
  • New type of factorial

uint fact(uint<m> x)<m>;

fact(3); // takes time 3+1

uint<n> x;

fact(x); // takes time n+1

pointer types
Pointer Types
  • Consider pointer types
    • int* Either a reference to an int or null
    • int@ Must be a reference
    • int<0> Must be a null pointer
  • Want to refine the type of a variable

// x has type int*

if (x == NULL) {

// x has type int<0>

} else {

// x has type int@

}

enforcement types
Enforcement types
  • Static representation of integer pointers

datatype ptr = Null | Ptr

intptr(m) =

case m of Null => int<0>

| Ptr => int@

  • If x : intptr(Ptr) then we know x is not NULL
refinement
Refinement

// suppose x : intptr(m)

if (x == NULL) {

// here we know that x : int<0>

// so thereforemmust beNull, or

// we’d get a contradiction

} else {

// know thatmisPtr

}

list enforcement type
List Enforcement Type

datatype list = Null | Cons( nat, list)

replist(m) =

case m of Null => int<0>

| Cons(val,next) =>

struct { const uint<val> val;

const replist(next) rest }@

using enforcement types
Using Enforcement Types

// if x has typereplist(m)

if (x == NULL) {

// again x : int<0>

} else {

// m must be Cons (val, next)

// x:{const int<val> val;

// const replist(next) rest }@

}

  • We’ve used a comparison in the dynamic code to increase our knowledge of the static representation
user defined size1
User-defined Size
  • Iterate over list, calculating a nat to represent execution time

fun time(m : list) =

case m of

Nil => 0

| Cons(val, next) =>

val+2+time(next)

example code
Example : Code

uint sumf (uint f(uint y)<y>;

replist(m) x; uint acc) <time(m)> {

if (x == null) return acc;

else {

// m must Cons( val, next)

// call to f takes time val + 1

uint acc2 = acc + f (x->val);

struct list* x2 = x -> next;

// recursive call takes time(next) + 1

sumf (f, x2, acc2);

}

}

other resources
Other Resources
  • “Effect notation” int f(int)<3> doesn’t generalize to resources that can be recovered (e.g. space)
  • Alternative: Augment the operational semantics with a virtual clock that winds down as the program executes
virtual clocks
Virtual Clocks
  • Function types specify starting and ending times
    • (int,12) f(int, 15) starts at time 15 and finishes at time 12
  • Use polymorphism to abstract starting times
    • (int, n) f(int,n+3) runs in 3 steps - it is equivalent to int f(int)<3>
recoverable resources
Recoverable Resources

Consider:

(int, n+12) f (int, n+15)

vs. (int, n) f (int,n+3)

If the resource is free-space:

  • the first function may allocate as many as 15 units, as long as it releases 12 of them before returning.
  • The second function only requires a minimum of 3 units of free-space to execute.
upper bound
Upper Bound
  • Sometimes it is enough just to know an upper bound of the running time.
  • It is an approximation to help when static analysis fails.
  • Add the instruction waste to the language to increment the virtual clock
  • No run-time effect
waste example
Waste example

bool member (int x; replist(m) w) <length(m)>{

if (w == NULL) return false;

else

// m=Cons( val , next)

if (x == w->val) {

waste <next>; return true;

} else {

return member( w->next );

}

}

talres
TALres
  • We have implemented a similar system within the Typed Assembly Language framework
  • Clock contained in a virtual register, decremented on backwards jumps
  • TAL already has a sophisticated type constructor language
    • Added sum and inductive kinds and refinement procedure for comparison instructions
    • Operations on static natural numbers
source language
Source Language
  • Prototype implementation: PopCron
    • Resembles C + timing annotations
    • No separation between static and dynamic languages
  • Compiler to TALres
    • creates representations of datatypes and their enforcement types
the real details
The real details
  • Static and dynamic language are really two levels of the same language
    • Static language is embedded in the dynamic language as a language of type constructors
    • Types of dynamic language are constants in the static language
    • Types of static language are referred to as kinds
more details
More details
  • Building block for refinement is

“virtual case analysis”

another example
Dynamic trees

union tree {

uint Leaf;

struct node Node;

}

struct Node {

const tree@ left;

const tree@ right;

}

Static representation

datatype tree =

Leaf of nat

| Node of tree * tree

Another Example
tree example
Tree example

size (t : tree) = case t of Leaf(x) => x +1

| Node (left, right) => size(left)

+ size(right) + 2

uint sumf (uint f(uint<k>)<k>; reptree(t) t) <size(t)> {

switch t {

case Leaf(x): return f(x);

case Node(x):

return sumf(x.left) + sumf(x.right);

}

}

enforcement type for trees
Enforcement type for trees

reptree (tree) =

case tree of

Leaf x =>

union { uint<x> Leaf; empty Node;}

| Node (left, right) =>

union {

empty Leaf;

struct { const reptree(left)@ left;

const reptree(right)@ right;

} Node;

}

virtual case
Virtual Case

switch t {

case Leaf(x):

// Suppose at runtime we could examine the static

// representation

case t of

Leaf (x) =>

This is the only branch that could occur

Node (x) =>

…as here x is of type empty

virtual case1
Virtual Case

switch t {

case Leaf(x):

// Since one branch is impossible we don’t need to

// specify it

vcase t of Leaf(x) =>

// binds x in following code

  • This case statement is virtual because we know what branch will be taken -- no run time effect
related work
Related Work
  • FX
    • Reistad and Gifford, 94
  • Sized Types
    • Hughes, Pareto, Sabry, 96
    • Chin Khoo, 00
  • PCC
    • Necula and Lee, 97
  • DML
    • Xi and Pfenning, 98
future work
Future Work
  • Extension of implementation to other resources
  • More flexible enforcement types
  • Stronger equational logic
  • Inference and analysis in source language