Analyse statique de programmes numériques avec calculs flottants

Transcription

1 Analyse statique de programmes numériques avec calculs flottants 4ème Rencontres Arithmétique de l Informatique Mathématique Antoine Miné École normale supérieure 9 février 2011 Perpignan 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 1 / 63

2 Outline Introduction 1 Introduction Main goals Theoretical background 2 Rational Abstractions Interval domain Polyhedra domain 3 Floating-Point Abstractions Floating-point semantics Floating-point interval domain Expression linearization Floating-point polyhedra 4 Conclusion 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 2 / 63

3 Introduction Introduction 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 3 / 63

4 Static analysis Introduction Main Goals Goal: static analysis [CousotCousot-ISP76] Static (automatic) discovery of dynamic (semantic) properties of programs. Applications: compilation and optimisation, e.g.: array bound check elimination alias analysis verification, e.g.: infer invariants prove the absence of run-time errors (division by zero, overflow, invalid array access) prove functional properties We focus here on numerical properties of numerical variables. 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 4 / 63

5 Introduction Main Goals Example: discovering numerical invariants Insertion Sort for i=1 to 99 do p := T[i]; j := i+1; while j <= 100 and T[j] < p do T[j-1] := T[j]; j := j+1; end; T[j-1] := p; end; 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 5 / 63

6 Introduction Main Goals Example: discovering numerical invariants Interval analysis: Insertion Sort for i=1 to 99 do i [1, 99] p := T[i]; j := i+1; i [1, 99], j [2, 100] while j <= 100 and T[j] < p do i [1, 99], j [2, 100] T[j-1] := T[j]; j := j+1; i [1, 99], j [3, 101] end; i [1, 99], j [2, 101] T[j-1] := p; end; = there is no out of bound array access 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 5 / 63

7 Introduction Main Goals Example: discovering numerical invariants Linear inequality analysis: Insertion Sort for i=1 to 99 do i [1, 99] p := T[i]; j := i+1; i [1, 99], j = i + 1 while j <= 100 and T[j] < p do i [1, 99], i + 1 j 100 T[j-1] := T[j]; j := j+1; i [1, 99], i + 2 j 101 end; i [1, 99], i + 1 j 101 T[j-1] := p; end; 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 5 / 63

8 Theoretical background Introduction Theoretical Background Abstract interpretation: unifying theory of program semantics [CousotCousot-POPL77] Provide theoretical tools to design and compare static analyses that: always terminate are approximate (solve undecidability and efficiency issues) are sound by construction (no behavior is omitted) Analysis design roadmap: 1 concrete semantics 2 abstract domains 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 6 / 63

9 Concrete semantics Introduction Theoretical Background Concrete semantics: most precise mathematical expression of the program behavior Example: from programs (CFGs) to equation systems entry X 1 = D (initial states) 1 X:=?(0,10) X 2 = C X :=?(0, 10) X 1 2 X 3 = C Y := 100 X 2 Y:=100 loop 3 X<0 C Y := Y + 10 X 5 invariant X>=0 6 X 4 = C X 0 X 3 4 X 5 = C X := X 1 X 4 X:=X 1 Y:=Y+10 X 6 = C X < 0 X 3 5 V = {X, Y} variables D = P(V Q) sets of environments X i D reachable environments at location i C c X models the effect of command c on X 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 7 / 63

10 Introduction Concrete semantics (cont.) Theoretical Background Semantics of (non-deterministic) expressions and commands. E e : (V Q) P(Q) E c ρ E?(c, c ) ρ E V ρ E e ρ E e 1 e 2 ρ (expression semantics) = { c } = { x c x c } = { ρ(v) } = { v v E e ρ } = { v 1 + v 2 v 1 E e 1 ρ, v 2 E e 2 ρ } {+,,, /} ( / v 2 0) C c : P(V Q) P(V Q) C V :=e X C e 0 X (command semantics) = { ρ[ V v ] ρ X, v E e ρ } = { ρ ρ X, v E e ρ, v 0 } (can be extended to actual programming languages!) 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 8 / 63

11 Introduction Concrete semantics (cont.) Theoretical Background equations have the form: X i = F i (X 1,..., X n ) (P(V Q),,, ) is a complete lattice all the F i are monotonic (A B = C c A C c B) Constructive version of Tarski s theorem by [Tarski-PJM55] and [CousotCousot-PJM79] the system has a least solution (least fixpoint of F i ) it is the limit of: X 0 i = X k+1 i = F i (X1 k,..., X n k ) for successor ordinals Xi o = δ<o X i δ for limit ordinals (many kinds of semantics can be expressed in fixpoint form [Cousot-ENTCS97]) 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 9 / 63

12 Undecidability Introduction Theoretical Background elements in P(V Q) are not computer-representable C and are not computable least solutions of equations are not computable (requiring transfinite iterations) = we use computable abstractions i.e.: computable sound over-approximations 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 10 / 63

13 Abstract domains Introduction Theoretical Background Abstract elements: D set of computer-representable elements γ : D D concretization approximation order: X Y = γ(x ) γ(y ) Abstract operators: C c : D D and : (D D ) D soundness: (C c γ)(x ) (γ C c )(X ) γ(x ) γ(y ) γ(x Y ) Fixpoint extrapolation : (D D ) D widening soundness: γ(x ) γ(y ) γ(x Y ) termination: sequence (Y i ) i N the sequence X 0 = Y 0, X i+1 = X i Y i+1 stabilizes in finite time: n < ω, X n+1 = X n Both semantics and algorithmic aspects. 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 11 / 63

14 Abstract analysis Introduction Theoretical Background Principles: compute entirely in D replace C c with C c in equations iterate, using widening at loop heads W X i, 0 X i, k+1 = { F i (X 1, k,..., X n, k ) = X i, k F i (X 1, k,..., X n, k ) if i / W if i W Theorem: the iterations stabilize in finite time δ < ω: X i, δ+1 = X i, δ the result is sound: X i γ(x i, δ ) 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 12 / 63

15 Introduction Theoretical Background Some existing numerical abstract domains Intervals Xi [a i, b i ] [CousotCousot-ISP76] Simple Congruences Xi a i [b i ] [Granger-JCM89] Linear Equalities i α ix i = β [Karr-AI76] Linear Congruences i α ix i β [γ] [Granger-TAPSOFT91] 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 13 / 63

16 Introduction Theoretical Background Some existing numerical abstract domains (cont.) Polyhedra i α ix i β [CousotHalbwachs-POPL78] Octagons ±Xi ± X j β [Miné-WCRE01] Ellipsoids αx 2 i + βx 2 j + γx i Y i δ [Feret-ESOP04] Varieties P( X) = 0, P R[V] [SankaranarayananAl-POPL04] 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 14 / 63

17 Introduction Precision vs. cost tradeoff Theoretical Background Example: three abstractions of the same set of points Worst-case time cost per operation wrt. number of variables: polyhedra: exponential octagons: cubic intervals: linear 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 15 / 63

18 Rational Abstractions Rational Numerical Abstract Domains 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 16 / 63

19 Rational Abstractions Interval Domain Interval Domain 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 17 / 63

20 Rational Abstractions Interval abstract elements Interval Domain Abstract elements: I = { (a, b) a Q { }, b Q {+ }, a b } (intervals as pairs of bounds) D Concretization: = (V I) { } γ( ) = γ(x ) = { ρ V, ρ(v) γ I (X (V)) } if X where γ I (a, b) = { x Q a x b } Order: X Y where (a, b) I (a, b ) { X = X, Y V, X (V) I Y (V) a a b b 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 18 / 63

21 Rational Abstractions Interval abstract operators Interval Domain Interval arithmetics in I I (a, b) (a, b) + I (c, d) (a, b) I (c, d) (a, b) I (c, d) (a, b) I (c, d). = ( b, a) = (a + c, b + d) = (a d, b c) = (min(ac, ad, bc, bd), max(ac, ad, bc, bd)) = (min(a, c), max(b, d)) Join in D X Y = X Y λv. X (V) I Y (V) if Y = if X = if X, Y (optimal, but not exact: γ(x ) γ(y ) γ(x Y )) 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 19 / 63

22 Interval assignment Rational Abstractions Interval Domain E e : D I (abstract expression evaluation, X ) E c X E?(c, c ) X E V X E e X E e 1 e 2 X = (c, c) = (c, c ) = X (V) = I E e X = E e 1 X I E e 2 X C c : D D C V :=e X = (abstract command semantics) { if X = X [ V E e X ] if X 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 20 / 63

23 Interval test example Rational Abstractions Interval Domain C X + Y Z 0 X, with X = {X (0, 10), Y (2, 10), Z (3, 5)} X (0, 10) + (, + ) - (, + ) Y (2, 10) Z (3, 5) X (0, 10) + (2, 20) - ( 3, 17) Y (2, 10) Z (3, 5) X (0, 10) + (2, 20) - ( 3, 0) Y (2, 10) Z (3, 5) X (0, 3) + (2, 5) - ( 3, 0) Y (2, 5) Z (3, 5) 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 21 / 63

24 Interval widening Rational Abstractions Interval Domain Classic widening in D X X Y where = (a, b) I (c, d) = Y λv. X (V) I Y (V) ({ a if a c otherwise, if Y = if X = if X, Y { b if b d + otherwise ) Unstable bounds are set to ± Widening with thresholds: Parametrized by a finite set T. If b < d, bump to the next value in T greater than d (or + ). 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 22 / 63

25 Rational Abstractions Interval analysis example Interval Domain Analysis example with W = {2} X< X:=0 X>=40 4 X:=X+1 l 1 2 = 0 = = 0 = 0 [0, 39] [0, 39] X 0 l More precisely, at the widening point: X 1 X 2 X 3 X 4 X 1 l X 2 l X 3 l X 4 l 2 = ([0, 0] ) = [0, 0] = [0, 0] 2 = [0, 0] ([0, 0] ) = [0, 0] [0, 0] = [0, 0] 2 = [0, 0] ([0, 0] [1, 1]) = [0, 0] [0, 1] = [0, + [ 2 = [0, + [ ([0, 0] [1, 40]) = [0, + [ [0, 40] = [0, + [ Note that the most precise interval abstraction would be X [0, 40] at 2, and X = 40 at 4. 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 23 / 63 X 5 l

26 Rational Abstractions Polyhedra Domain Polyhedra Domain 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 24 / 63

27 Polyhedra domain Rational Abstractions Polyhedra Domain Domain proposed by [CousotHalbwachs-POPL78] to infer conjunctions of linear inequalities j ( n i=1 α ijv i β j ). Abstract elements: LinCons = linear constraints over V with coefficients in Q D Concretization: = P finite (LinCons) γ(x ) = { ρ V Q c X, ρ = c } γ(x ) is a closed convex polyhedron of (V Q) Q V γ(x ) may be empty, bounded, or unbounded γ is not injective 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 25 / 63

28 Polyhedra algorithms Rational Abstractions Polyhedra Domain Fourier-Motzkin elimination: Fourier(X, V k ) eliminates V k from all the constraints in X : Fourier(X, V k ) = { ( i α iv i β) X α k = 0 } { ( α k )c+ + α + k c c + = ( i α+ i V i β + ) X, α + k > 0, c = ( i α i V i β ) X, α k < 0 } Semantics γ(fourier(x, V k )) = { ρ[v k v] v Q, ρ γ(x ) } i.e., forget the value of V k 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 26 / 63

29 Polyhedra algorithms Rational Abstractions Polyhedra Domain Linear programming: [Schrijver-86] simplex(x, α) = min { i α iρ(v i ) ρ γ(x ) } Application: remove redundant constraints: for each c = ( i α iv i β) X if β simplex(x \ {c}, α), then remove c from X (e.g., Fourier causes a quadratic growth in constraint number, most of which are redundant) Note: calling simplex many times can be costly use fast syntactic checks first check against the bounding-box first use simplex as a last resort 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 27 / 63

30 Rational Abstractions Polyhedra abstract operators Polyhedra Domain Order: X Y X = Y ( i α iv i β) Y, simplex(x, α) β γ(x ) γ(y ) X Y Y X Join: [BenoyKing-LOPSTR96] We introduce temporaries V X j, V Y j, σ X, σ Y : = X Y Fourier( { ( j α jv X j βσ X 0) ( j α jv j β) X } { ( j α jv Y j βσ Y 0) ( j α jv j β) Y } { V j = V X j + V Y j V j V } { σ X 0, σ Y 0, σ X + σ Y = 1 }, { V X j, V Y j V j V } { σ X, σ Y } ) γ(x Y ) is the topological closure of the convex hull of γ(x ) and γ(y ) (optimal). 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 28 / 63

31 Rational Abstractions Polyhedra Domain Polyhedra abstract operators (cont.) Precise abstract commands: C i α iv i + β 0 X (exact) = X {( i α iv i + β 0)} C V j := [, + ] X = Fourier(X, V j )) C V j := i α iv i + β X = subst(v V i, Fourier((X {V = i α iv i + β}), V j )) Fallback abstract commands: C e 0 X = X (coarse but sound) C V j := e X = Fourier(X, V j ) alternate solution: apply interval abstract commands to the bounding box 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 29 / 63

32 Polyhedra widening Rational Abstractions Polyhedra Domain Classic widening in D X Y = { c X Y {c} } { c Y c X, X = (X \ c ) {c} } suppress unstable constraints c X, Y {c} add back constraints c Y equivalent to those in X i.e., when c X, X = (X \ c ) {c}. (X and Y must have no redundant constraint) Example: 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 30 / 63

33 Analysis example Rational Abstractions Polyhedra Domain Rate limiter Y:=0; while true do X:=?(-128,128); D:=?(0,16); S:=Y; Y:=X; R:=X-S; if R+D<=0 then Y:=S-D fi; if R-D>=0 then Y:=S+D fi od X: input signal Y: output signal S: last output R: delta Y-S D: max. allowed for R Polyhedra analysis: result: at, Y [ 128, 128] to prove, e.g., Y 128, the analysis needs to: represent the properties R = X S and R D combine them to deduce S X D, and then Y = S D X 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 31 / 63

34 Analysis example Rational Abstractions Polyhedra Domain Rate limiter Y:=0; while true do X:=?(-128,128); D:=?(0,16); S:=Y; Y:=X; R:=X-S; if R+D<=0 then Y:=S-D fi; if R-D>=0 then Y:=S+D fi od X: input signal Y: output signal S: last output R: delta Y-S D: max. allowed for R Interval analysis: iterations without widening: X,0 X,1 X,2... X,n Y = 0 Y 144 Y Y n i.e., not bound on Y can be found. 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 31 / 63

35 Floating-point abstractions Floating-point abstractions 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 32 / 63

36 Floating-point abstractions Floating-point uses Two independent problems: Implement the analyzer using floating-point goal: trade precision for efficiency exact rational arithmetics can be costly coefficients can grow large (polyhedra) Analyze floating-point programs goal: catch run-time errors caused by rounding (overflow, division by 0,... ) Also: a floating-point analyzer for floating-point programs. Challenge: how to stay sound? 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 33 / 63

37 Floating-point abstractions Floating-point semantics Floating-point semantics 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 34 / 63

38 Floating-point abstractions Floating-point numbers Floating-point semantics IEEE standard is the most widespread format (supported by most processors and programming languages) IEEE Binary representation: a number is a triple s, e, f a 1-bit sign s a x-bit exponent e, with a bias (e represents e bias) a p-bit fraction f =.b 1... b p, (f represents i 2 i b i ) IEEE format examples given by the choice of x, bias, p: x = 8 32-bit single precision float: bias = 127 p = 23 Other widespread formats: 64-bit double, 80-bit double extended, 128-bit quad 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 35 / 63

39 Floating-point abstractions Floating-point representation Floating-point semantics Semantics s, e, f represents either: a normalized number: ( 1) s 2 e bias 1.f (if 1 e 2 x 2); a denormalized number: ( 1) s 2 1 bias 0.f (if e = 0, f 0); +0 or 0 (if e = 0, f = 0); + or (if e = 2 x 1, f = 0); an error code NaN (if e = 2 x 1, f 0). Visual representation (positive part) +0 mf Mf + mf Mf denormalized normalized = 2 1 bias p smallest positive = (2 2 p ) 2 2x bias 2 largest non- 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 36 / 63

40 Floating-point abstractions Floating-point computations Floating-point semantics The set of floating-point numbers is not closed under +,,, /: every result is rounded to a representable float, an overflow or division by 0 generates + or (overflow); small numbers are truncated to +0 or 0 (underflow); some operations are invalid (0/0, (+ ) + ( ), etc.) and return NaN. Simplified semantics: overflows and NaNs halt the program with an error Ω, rounding and underflow are not errors, we do not distinguish between +0 and 0. = variable values live in a finite subset F of Q, expression values live in F {Ω}. 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 37 / 63

41 Floating-point abstractions Floating-point expressions Floating-point semantics Floating-point expressions exp f exp f ::= [c, c ] constant range c, c F, c c V variable V V exp f negation exp f r exp f operator {,,, } (we use circled operators to distinguish them from operators in Q) 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 38 / 63

42 Floating-point abstractions Concrete semantics of expressions Floating-point semantics Semantics of rounding: R r : Q F {Ω}. 4 rounding modes r: towards +,, 0, or to nearest n. Example inition: R + (x) = R (x) = { min { y F y x } if x Mf Ω if x > Mf { max { y F y x } if x Mf Ω if x < Mf Notes: x, r, R (x) R r (x) R + (x) r, R r is monotonic 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 39 / 63

43 Floating-point abstractions Floating-point semantics Concrete semantics of expressions (cont.) E e f : (V F) P(F {Ω}) (expression semantics) E V ρ = { ρ(v) } E [c, c ] ρ = { x F c x c } E e f ρ = { x x E e f ρ F } ({Ω} E e f ρ) E e f r e f ρ = { R r (x y) x E e f ρ F, y E e f ρ F } { Ω if Ω E e f ρ E e f ρ } { Ω if 0 E e f ρ and = } C c : P(V F) P((V F) {Ω}) (command semantics) C X := e f X = { ρ[ X v ] ρ X, v E e f ρ F } ({Ω} E e f X ) C e f 0 X = { ρ ρ X, v E e f ρ F, v 0 } ({Ω} E e f X ) 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 40 / 63

44 Floating-point abstractions Floating-point interval domain Floating-point interval domain 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 41 / 63

45 Floating-point abstractions Floating-point interval domain Floating-point interval abstract elements Goals: analyze floating-point programs abstracted using a floating-point implementation report infinities and NaN as errors Abstract elements: I = { (a, b) a, b F, a b } (floating-point bounds) D Concretization: = (V I) { } γ( ) = γ(x ) = { ρ V, ρ(v) γ I (X (V)) } if X where γ I (a, b) = { x F a x b } 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 42 / 63

46 Floating-point abstractions Floating-point interval domain Floating-point interval domain Interval arithmetics I (a, b) (a, b) I (a, b ) (a, b) I (a, b ) (a, b) I (a, b ) = ( b, a) = (a a, b + b ) = (a b, b + a ) = (min(a a, a b, b a, b b ), max(a + a, a + b, b + a, b + b )) (a, b) I (a, b ) = (min(a, a ), max(b, b )) I sets unstable bounds to ± Mf (we suppose r is unknown and assume a worst case rounding) Error management If some bound in E e f evaluates to ± or NaN, we report an alarm to the user and continue the evaluation with ( Mf, Mf ) 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 43 / 63

47 Floating-point abstractions Floating-point interval domain Floating-point interval analysis example Filter with reinitialisation Z:=0; while true do if?(0,1) then Z:=?(-12,12) fi; Z:=(0.3? Z)??(-10,10) od in Q: Z < 10/0.7 ( 14.29). in F: Z B, with B = R + (10/0.7). Interval analysis: using a widening with thresholds T: Z min { x T x B }. = proof of absence of overflow if T has a value larger than B. 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 44 / 63

48 Floating-point abstractions Expression linearization Expression linearization 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 45 / 63

49 Floating-point abstractions Expression linearization Floating-point issues in relational domains Relational domains assume many powerful properties on Q: associativity, distributivity,... that are not true on F! Example: Fourier-Motzkin elimination X Y c Y Z d = X Z c + d X n Y c Y n Z d X n Z c n d (X = 1, Y = 10 38, Z = 1, c = X n Y = 10 38, d = Y n Z = 10 38, c n d = 0, X n Z = 2 > 0) We cannot manipulate float expressions as easily as rational ones! Solution: keep representing and manipulating rational expressions abstract float expressions from programs into rational ones feed them to a rational abstract domain (optional) implement the rational domain using floats 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 46 / 63

50 Floating-point abstractions Affine interval forms Expression linearization We put expressions in affine interval form: [Miné-ESOP04] Semantics: exp l ::= [a 0, b 0 ] + k [a k, b k ] V k E e l ρ = { c 0 + k c k ρ(v k ) i, c i [a i, b i ] } (evaluated in Q) Advantages: affine expressions are easy to manipulate interval coefficients allow non-determinism in expressions, hence, the opportunity for abstraction intervals can easily model rounding errors easy to design algorithms for C X :=e l and C e l 0 in most domains 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 47 / 63

51 Floating-point abstractions Affine interval form algebra Expression linearization Operations on affine interval forms: adding and subtracting two forms multiplying and dividing a form by an interval Using interval arithmetics + I, I, I : (i 0 + k i k V k ) (i 0 + k i k V k) = (i 0 + I i 0 ) + k (i k + I i k ) V k i (i 0 + k i k V k ) = (i I i 0 ) + k (i I i k ) V k... Projection: π k : D exp l We suppose we are given an abstract interval projection operator π k such that: π k (X ) = [a, b] such that [a, b] { ρ(v k ) ρ γ(x ) }. 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 48 / 63

52 Floating-point abstractions Linearization of rational expressions Expression linearization Intervalization: ι : (exp l D ) exp l Intervalization flattens the expression into a single interval: ι(i 0 + k i k V k, X ) = i 0 + I I, k (i k I π k (X )). Linearization without rounding errors: Defined by induction on the syntax of expressions: l(v, X ) = [1, 1] V l([a, b], X ) = [a, b] l(e 1 +e 2, X ) = l(e 1, X ) l(e 2, X ) l(e 1 e 2, X ) = l(e 1, X ) l(e 2, X ) l(e 1 /e 2, X ) = l(e 1, X ) ι(l(e 2, X ), X ) l(e 1 e 2, X ) = can be l : (exp D ) exp l { either ι(l(e1, X ), X ) l(e 2, X ) or ι(l(e 2, X ), X ) l(e 1, X ) 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 49 / 63

53 Floating-point abstractions Expression linearization Linearization of floating-point expressions Rounding an affine interval form: if the result is normalized: we have a relative error ε with magnitude 2 23 : ε([a 0, b 0 ] + k [a k, b k ] V k ) = max( a 0, b 0 ) [ 2 23, 2 23 ] + k (max( a k, b k ) [ 2 23, 2 23 ] V k ) if the result is denormalized, we have an absolute error ω = [ 2 159, ]. = we sum these two sources of rounding errors Linearization with rounding errors: l : (exp f D ) exp l l(e 1 r e 2, X ) = l(e 1, X ) l(e 2, X ) ε(l(e 1, X )) ε(l(e 2, X )) ω l(e 1 r e 2, X ) = ι(l(e 1, X ), X ) (l(e 2, X ) ε(l(e 2, X ))) ω etc. 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 50 / 63

54 Floating-point abstractions Expression linearization Applications of the floating-point linearization Soundness of the linearization e, X D, ρ γ(x ), if Ω / E e ρ, then E e ρ E l(e, X ) ρ Application: C V :=e X Note: check that Ω / E e ρ for ρ γ(x ) with interval arithmetic compute C V :=e X as C V :=l(e, X ) X (use C V :=[ Mf, Mf ] X if Ω E e ρ) I, I, I, I are sound abstractions of + I, I, I, / I = use I, etc. to get a sound approximation of l(e, X ) in floats 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 51 / 63

55 Floating-point abstractions Sound floating-point polyhedra Sound floating-point polyhedra 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 52 / 63

56 Floating-point abstractions Sound floating-point polyhedra Sound floating-point polyhedra Algorithms to adapt: [ChenAl-APLAS08] linear programming: simplex f (X, α) simplex(x, α) simplex(x, α) = min { k α kρ(v k ) ρ γ(x ) } Fourier-Motzkin elimination: Fourier f (X, V k ) = Fourier(X, V k ) Fourier(X, V k ) = { ( i α iv i β) X α k = 0 } { ( α k )c+ + α + k c c + = ( i α+ i V i β + ) X, α + k > 0, c = ( i α i V i β ) X, α k < 0 } 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 53 / 63

57 Floating-point abstractions Sound floating-point polyhedra Sound floating-point linear programming Guaranteed linear programming: [NeumaierShcherbina-MP04] Goal: under-approximate µ = min { c x M x b } knowing that x [ x l, x h ] (bounding-box for γ(x )). compute any approximation µ of the dual problem: µ µ = max { b y t M y = c, y 0 } and the corresponding vector y (e.g. using an off-the-shelf solver; µ may over-approximate or under-approximate µ) compute with intervals safe bounds [ r l, r h ] for A y c: [ r l, r h ] = ( t A I y) I c and then: ν = inf(( b I y) I ([ r l, r h ] I [ x l, x h ])) then: ν µ. 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 54 / 63

58 Floating-point abstractions Sound floating-point polyhedra Sound floating-point Fourier-Motzkin elimination Given: c + = ( i α + i V i β + ) with α + k > 0 c = ( i α i V i β ) with α k < 0 a bounding-box of γ(x ): [ x l, x h ] We wish to compute i k α iv i β in F implied by ( α k )c+ + α + k c in γ(x ). normalize c + and c using interval arithmetics: { Vk + i k (α+ i I α + k )V i β + I α + k V k + i k (α i I ( α k ))V i β I ( α k ) (interval affine forms) add them using interval arithmetics: i k [a i, b i ]V i [a 0, b 0 ] where [a i, b i ] = (α + i I α + k ) I (α i I α k ), [a 0, b 0 ] = (β + I α + k ) I (β I α k ). 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 55 / 63

59 Floating-point abstractions Sound floating-point polyhedra Sound floating-point Fourier-Motzkin elimination (cont.) linearize the interval linear form into i k α iv i β where { αi [a i, b i ] β = sup ([a 0, b 0 ] I I, i k ( α i I [a i, b i ] ) I [ x l, x h ] ) Soundness: For all choices of α i [a i, b i ], i k α iv k β holds in Fourier(X, V k ). (e.g. α i = (a i n b i ) 2) 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 56 / 63

60 Floating-point abstractions Consequences of rounding Sound floating-point polyhedra Precision loss: Projection: γ(fourier(x, V k )) { ρ[v k v] v Q, ρ γ(x ) } = C V k := [, + ] γ(x ) Order: X Y = γ(x ) γ(y ) Join: ( ) γ(x Y ) ConvexHull(γ(X ) γ(y )) ( ) Efficiency loss: cannot remove all redundant constraints 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 57 / 63

61 Floating-point abstractions Floating-point polyhedra widening Sound floating-point polyhedra Widening : X Y = { c X Y {c} } (drop { c Y c X, X = (X \ c ) {c}} as X and Y may have redundant constraints) Stability improvement: robust strategies to choose α i [a i, b i ] during Fourier-Motzkin: choose simple α i (e.g., integer nearest (a i + b i )/2) reuse the same (or a multiple of) α i used for other variables 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 58 / 63

62 Conclusion Conclusion 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 59 / 63

63 Conclusion Abstraction summary Floating-point polyhedra analyzer for floating-point programs expression abstraction environment abstraction float expression e f linearization P(V F) affine form e l in Q abstract domain float implementation polyhedra in Q affine form e l in F float implementation polyhedra in F widening polyhedra in F 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 60 / 63

64 Conclusion Academic implementation Interproc: on-line analyzer for a toy language, using Apron. 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 61 / 63

65 Conclusion Industrial application Astrée static analyzer: [BertraneAl-AIAA10] developed at ENS, now industrialized by analyzes embedded critical control/command C code checks for run-time errors (arithmetics, arrays, pointers) applied to industrial Airbus code, up to 1 M lines zero alarm, 14h computation time Based on abstract interpretation: uses intervals and octagons (no polyhedra) and many more abstract domains (some domain-specific) uses linearization of float expressions More information: 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 62 / 63

66 Conclusion The End 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 63 / 63

67 Bibliography Bibliography 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 64 / 63

68 Bibliography Bibliography [Tarski-PJM55] A. Tarski. A lattice theoretical fixpoint theorem and its applications. In Pacific J. Math., 5 (1995), [CousotCousot-ISP76] P. Cousot & R. Cousot. Static determination of dynamic properties of programs. In Proc. of the 2d Int. Symp. on Prog. Dunod, [CousotCousot-POPL77] P. Cousot & R. Cousot. Abstract interpretation: a unified lattice model for static analysis of programs by construction or approximation of fixpoints. In Proc. of POPL 77, , ACM Press, [CousotCousot-PJM79] P. Cousot & R. Cousot. Constructive versions of Tarski s fixed point theorems. In Pacific J. Math., 82:1 (1979), [CousotHalbwachs-POPL78] P. Cousot & N. Halbwachs. Automatic discovery of linear restraints among variables of a program. In Proc. of POPL 78, ACM Press, /02/2011 Analyse statique de programmes numériques Antoine Miné p. 65 / 63

69 Bibliography Bibliography (cont.) [Schrijver-86] A. Schrijver. Theory of linear and integer programming. John Wiley & Sons, Inc., [BenoyKing-LOPSTR96] F. Benoy & A. King. Inferring argument size relationships with CLP(R). In Proc. of LOPSTR 96, vol of LNCS, pp Springer, [Cousot-ENTCS97] P. Cousot. Constructive design of a hierarchy of semantics of a transition system by abstract interpretation. In ENTCS 6, 25 p., [Miné-AST01] A. Miné. The octagon abstract domain. In AST 01, pp IEEE CS Press, [Miné-ESOP04] A. Miné. Relational abstract domains for the detection of floating-point run-time errors. In Proc. of ESOP 04, vol of LNCS, pp Springer, /02/2011 Analyse statique de programmes numériques Antoine Miné p. 66 / 63

70 Bibliography Bibliography (cont.) [NeumaieShcherbina-MP04] A. Neumaier & O. Shcherbina. Safe bounds in linear and mixed-integer linear program ming. In Math. Program., 99(2): (2004). [Miné-ESOP04] A. Miné. Relational abstract domains for the detection of floating-point run-time errors. In Proc. of ESOP 04, vol of LNCS, pp Springer, [ChenAl-APLAS08] L. Chen, A. Miné & P. Cousot. A sound floating-point polyhedra abstract domain. In Proc. of APLAS 08, vol of LNCS, pp Springer, [BertraneAl-AIAA10] J. Bertrane, P. Cousot, R. Cousot, J. Feret, L. Mauborgne, A. Miné, X. Rival. Static Analysis and Verification of Aerospace Software by Abstract Interpretation. In AIAA Infotech@Aerospace (I@A 2010), 38 p. 9/02/2011 Analyse statique de programmes numériques Antoine Miné p. 67 / 63