1. 1 Iterative Program Analysis Part II Mathematical Background Mooly Sagiv http://www.cs.tau.ac.il/~msagiv/courses/pa11.html Tel Aviv University 640-6706 Textbook: Principles of Program Analysis Appendix A

2. Content u Mathematical Background u Chaotic Iterations u Soundness, Precision and more examples next week

3. Mathematical Background u Declaratively define –The result of the analysis –The exact solution –Allow comparison

4. Posets u A partial ordering is a binary relation  : L  L  {false, true} –For all l  L : l  l (Reflexive) –For all l 1, l 2, l 3  L : l 1  l 2, l 2  l 3  l 1  l 3 (Transitive) –For all l 1, l 2  L : l 1  l 2, l 2  l 1  l 1 = l 2 (Anti-Symmetric) u Denoted by (L,  ) u In program analysis –l 1  l 2  l 1 is more precise than l 2  l 1 represents fewer concrete states than l 2 u Examples –Total orders (N,  ) –Powersets (P(S),  ) –Powersets (P(S),  ) –Constant propagation

5. Posets u More notations –l 1  l 2  l 2  l 1 –l 1  l 2  l 1  l 2  l 1  l 2 –l 1  l 2  l 2  l 1

6. Upper and Lower Bounds u Consider a poset (L,  ) u A subset L’  L has a lower bound l  L if for all l’  L’ : l  l’ u A subset L’  L has an upper bound u  L if for all l’  L’ : l’  u u A greatest lower bound of a subset L’  L is a lower bound l 0  L such that l  l 0 for any lower bound l of L’ u A lowest upper bound of a subset L’  L is an upper bound u 0  L such that u 0  u for any upper bound u of L’ u For every subset L’  L: –The greatest lower bound of L’ is unique if at all exists »  L’ (meet) a  b –The lowest upper bound of L’ is unique if at all exists »  L’ (join) a  b

7. Complete Lattices u A poset (L,  ) is a complete lattice if every subset has least and upper bounds u L = (L,  ) = (L, , , , ,  ) –  =   =  L –  =  L =   u Examples –Total orders (N,  ) –Powersets (P(S),  ) –Powersets (P(S),  ) –Constant propagation

8. Complete Lattices u Lemma For every poset (L,  ) the following conditions are equivalent –L is a complete lattice –Every subset of L has a least upper bound –Every subset of L has a greatest lower bound

9. Cartesian Products u A complete lattice (L 1,  1 ) = (L 1, ,  1,  1,  1,  1 ) u A complete lattice (L 2,  2 ) = (, ,  2,  2,  2,  2 ) u Define a Poset L = (L 1  L 2,  ) where –(x 1, x 2 )  (y 1, y 2 ) if »x 1  y 1 and »x 2  y 2 u L is a complete lattice

10. Finite Maps u A complete lattice (L 1,  1 ) = (L 1, ,  1,  1,  1,  1 ) u A finite set V u Define a Poset L = (V  L 1,  ) where –e 1  e 2 if for all v  V »e 1 v  e 2 v u L is a complete lattice

11. Chains u A subset Y  L in a poset (L,  ) is a chain if every two elements in Y are ordered –For all l 1, l 2  Y: l 1  l 2 or l 2  l 1 u An ascending chain is a sequence of values –l 1  l 2  l 3  … u A strictly ascending chain is a sequence of values –l 1  l 2  l 3  … u A descending chain is a sequence of values –l 1  l 2  l 3  … u A strictly descending chain is a sequence of values –l 1  l 2  l 3  … u L has a finite height if every chain in L is finite u Lemma A poset (L,  ) has finite height if and only if every strictly decreasing and strictly increasing chains are finite

12. Monotone Functions u A poset (L,  ) u A function f: L  L is monotone if for every l 1, l 2  L: –l 1  l 2  f(l 1 )  f(l 2 )

13. Fixed Points u A monotone function f: L  L where (L, , , , ,  ) is a complete lattice u Fix(f) = { l: l  L, f(l) = l} u Red(f) = {l: l  L, f(l)  l} u Ext(f) = {l: l  L, l  f(l)} –l 1  l 2  f(l 1 )  f(l 2 ) u Tarski’s Theorem 1955: if f is monotone then: – lfp(f) =  Fix(f) =  Red(f)  Fix(f) – gfp(f) =  Fix(f) =  Ext(f)  Fix(f)   f(  ) f(  ) f2()f2() f2()f2() Fix(f) Ext(f) Red(f) gfp(f) lfp(f)

14. Example Constant Propagation x =3 nop  e.e[x  3]  e.e 1 2 3 CP(1) = [x  0] CP(2) = CP(1)[x  3]  CP(2) CP(3) = CP(2) exit  e.e

15. Chaotic Iterations u A lattice L = (L, , , , ,  ) with finite strictly increasing chains u L n = L  L  …  L u A monotone function f: L n  L n u Compute lfp(f) u The simultaneous least fixed of the system {x[i] = f i (x) : 1  i  n } x := ( , , …,  ) while (f(x)  x ) do x := f(x) for i :=1 to n do x[i] =  WL = {1, 2, …, n} while (WL   ) do select and remove an element i  WL new := f i (x) if (new  x[i]) then x[i] := new; Add all the indexes that directly depends on i to WL

16. Chaotic Iterations u L n = L  L  …  L u A monotone function f: L n  L n u Compute lfp(f) u The simultaneous least fixed of the system {x[i] = f i (x) : 1  i  n } u Minimum number of non-constant u Maximum number of  for i :=1 to n do x[i] =  WL = {1, 2, …, n} while (WL   ) do select and remove an element i  WL new := f i (x) if (new  x[i]) then x[i] := new; Add all the indexes that directly depends on i to WL

17. Specialized Chaotic Iterations System of Equations S = df entry [s] =  df entry [v] =  {f(u, v) (df entry [u]) | (u, v)  E } F S :L n  L n F S (X)[s] =  F S (X)[v] =  {f(u, v)(X[u]) | (u, v)  E } lfp(S) = lfp(F S )

18. Specialized Chaotic Iterations Chaotic(G(V, E): Graph, s: Node, L: Lattice,  : L, f: E  (L  L) ){ for each v in V to n do df entry [v] :=  df[s] =  WL = {s} while (WL   ) do select and remove an element u  WL for each v, such that. (u, v)  E do temp = f(e)(df entry [u]) new := df entry (v)  temp if (new  df entry [v]) then df entry [v] := new; WL := WL  {v}

19. z =3 x =1 while (x>0) if (x=1) y =7y =z+4 x=3 print y  e.e[z  3]  e.e[x  1]  e. if x >0 then e else   e. if e x  0 then e else   e. e  [x  1, y , z  ]  e. if e x  0 then e else   e.e[y  7]  e.e[y  e(z)+4]  e.e[x  3]  e.e  1 2 3 4 5 6 7 8 [x  0, y  0, z  0] WLdf entry ]v] {1} {2} df[2]:=[x  0, y  0, z  3] {3} df[3]:=[x  1, y  0, z  3] {4} df[4]:=[x  1, y  0, z  3] {5} df[5]:=[x  1, y  0, z  3] {7} df[7]:=[x  1, y  7, z  3] {8} df[8]:=[x  3, y  7, z  3] {3} df[3]:=[x , y , z  3] {4} df[4]:=[x , y , z  3] {5,6} df[5]:=[x  1, y , z  3] {6,7} df[6]:=[x , y , z  3] {7} df[7]:=[x , y  7, z  3]

20. Complexity of Chaotic Iterations u Parameters: –n the number of CFG nodes –k is the maximum outdegree of edges –A lattice of height h –c is the maximum cost of »applying f (e) »  »L comparisons u Complexity O(n * h * c * k)

21. Conclusions u Chaotic iterations is a powerful technique u Easy to implement u Rather precise u But expensive –More efficient methods exist for structured programs