1. Part 1: Positive Equality for Uninterpreted functions in Eager Encoding

2. – 2 – Eliminating Function applications Two applications of an uninterpreted function f in a formula f( x 1 ) and f( x 2 )Ackermann’sEncoding f( x 1 ) vf 1 f( x 2 ) vf 2 x 1 = x 2 vf 1 = vf 2 x 1 = x 2  vf 1 = vf 2 Bryant, German, Velev’s Encoding f( x 1 ) vf 1 f( x 2 ) ITE( x 1 = x 2, vf 1, vf 2 )

3. – 3 – Positive Equality Optimization Goal Replace as many of the vf i variables with constant values Exploit the positive structure of the formula Overall Benefit The function-free formula has smaller number of integer variables Reduces the number of interpretations to check for validity

4. – 4 – Eliminating Function applications Two applications of an uninterpreted function f in a formula f( x 1 ) and f( x 2 )Ackermann’sEncoding f( x 1 ) vf 1 f( x 2 ) vf 2 x 1 = x 2 vf 1 = vf 2 x 1 = x 2  vf 1 = vf 2 Bryant, German, Velev’s Encoding f( x 1 ) vf 1 f( x 2 ) ITE( x 1 = x 2, vf 1, vf 2 ) Favors positive equality analysis

5. – 5 – EUF Logic of Equality with Uninterpreted FunctionsTerms ITE(F, T 1, T 2 ) If-then-else f (T 1, …, T k ) Function applicationFormulas  F, F 1  F 2, F 1  F 2 Boolean connectives T 1 = T 2 Equation p (T 1, …, T k ) Predicate application Special Cases v Domain variable (order-0 function) a Propositional variable (order-0 predicate)

6. – 6 – EUF and small-model property Small Model Property for Validity Small Model Property for Validity [Ackermann ’54] Suffices to consider a domain with k values k is the number of distinct function application terms in the formula Number of cases (interpretations) to check: k! xy gg ff = =    (x=y)  (f(g(x)) = f(g(y)) Function-application terms: { x, y, g(x), g(y), f(g(x), f(g(y) } k = 6

7. – 7 – Positive Equality for EUF Classify formulas, terms, functions into Positive (p) General (g) xy gg ff = =    (x=y)  (f(g(x)) = f(g(y)) General (g) Functions x,y Positive (p) Functionsf,g p-formulas g-formulas p-terms [Bryant, German, Velev CAV’99] Positive (p) formulas Negated even no. of times Do not control ITE Positive (p) terms Never appears in a g- formula equation Positive (p) function symbols All applications are p-terms

8. – 8 – Maximally Diverse Interpretations An interpretation I is maximally diverse if: For any p-function symbol f 1. I [ f(T1) = f(T2) ] iff I [ T1=T2 ] 2. I [ f(T) ]  I [ g(U) ], for any other function symbol g where f(T1), f(T2), g(U) are terms in the formula h xy =  =  g g g h TermsEqual? xy Potentially g (x)g (y) Only if x = y g (x)y No

9. – 9 – Maximally Diverse Interpretations An interpretation I is maximally diverse if: For any p-function symbol f 1. I [ f(T 1 ) = f(T 2 ) ] iff I [ T 1 =T 2 ] 2. I [ f(T 1 ) ]  I [ g(U) ], for any other function symbol g where f(T 1 ), f(T 2 ), g(U) are terms in the formulaProperty Formula valid if and only if true under all maximally diverse interpretations

10. – 10 – Justification of Maximal Diversity Property For a formula For a formula F For any interpretation I, there is a maximally diverse interpretation J, such that J [ F ]  I [ F ] h xy =  =  g g g h Create Worst Case for Validity Falsify positive equation Create Worst Case for Validity Falsify positive equation Function applications yield distinct results Create Worst Case for Validity Falsify positive equation Function applications yield distinct results Function arguments distinct

11. – 11 – Exploiting Positive Equality Property P-function symbol f Introduce variables vf 1, …, vf n during elimination Consider only diverse interpretations for variables vf 1, …, vf n vf i  v for any other variable vExample Assuming vf 1  vf 2 : x1x1 x2x2 vf 1 vf 2 TFTF = = iff x 1 =x 2 f(x 1 ) f(x 2 )

12. – 12 – Summary: Positive equality optimization  Eliminate function applications  Introduce vf 1, …, vf n while eliminating function symbol f  For a p-function symbol f  Replace vf 1, …, vf n with distinct constants  The only variables in the function-free formula are the vf i variables for g function symbols m = number of g-function applications

13. – 13 – Positive Equality for EUF xy gg ff = =    (x=y)  (f(g(x)) = f(g(y)) General (g) Functionsx,yPositiveFunctionsf,g Property Number of interpretations to consider = m! m = number of g-function applications

14. – 14 – Positive Equality for EUF  (x=y)  (f(g(x)) = f(g(y)) General (g) Functionsx,yPositiveFunctionsf,g Property Number of interpretations to consider = m! m = number of g-function applications Function-application terms: { x, y, g(x), g(y), f(g(x)), f(g(y)) } p applications: { g(x), g(y), f(g(x)), f(g(y)) } g applications: { x,y } m = 2 Search Space reduced from 6! to 2!

15. – 15 – Application of positive equality Pipelined processor verification Bryant, German and Velev CAV’99, Velev and Bryant DAC’00,.. Observation: Most uninterpreted functions which appear in pipeline data-path are p-functions E.g. ALU, Incrementer for PC, …. Other Infinite-state system verification Bryant, Lahiri, Seshia CAV’02 Improves efficiency in benchmarks from cache-coherence verification, out-of-order processors, software benchmarks

16. – 16 – Impact of Positive Equality ModelInitial formula size UCLID w/ p-eq. (s) UCLID w/o p-eq. (s) SVC time (s) Out-of-order proc 392961.90149.464257.3 Cache coherence 393961.08 > 1 hr> 1 day DLX pipeline63913.22 1897> 1 day Positive equality can be exploited to improve performance [Bryant, Lahiri, Seshia CAV’02]

17. – 17 – Ackermann’s encoding and positive equality Two applications of an uninterpreted function f in a formula f( x 1 ) and f( x 2 ) Can’t assign distinct values to vf 1, vf 2 for p-function symbol f x 1 = x 2 Ignores the case when x 1 = x 2Ackermann’sEncoding f( x 1 ) vf 1 f( x 2 ) vf 2 x 1 = x 2 vf 1 = vf 2 x 1 = x 2  vf 1 = vf 2

18. – 18 – Limitation of positive equality analysis Limitation of previous approach Not “robust” Entire analysis fails even when a single application is negative x =    (f(x)=x)  (f(f(f(f(x)))) = f(f(f((x))) GeneralFunctionsx,f PositiveFunctions f f f = f p-applications: {} g-applications: { x, f(x), f 2 (x), f 3 (x), f 4 (x) } Function-application terms: { x, f(x), f 2 (x), f 3 (x), f 4 (x) }

19. – 19 – Robust Positive Equality Analysis Look at each application instead of function symbols Finer granularity for exploiting positive equality [Lahiri, Bryant, Goel, Talupur TACAS’04] x =    (f(x)=x)  (f(f(f(f(x)))) = f(f(f((x))) General Functions Functionsx,f PositiveFunctions f f f = f p-terms: { f 2 (x), f 3 (x), f 4 (x) } g-terms: { x, f(x) } Function-application terms: { x, f(x), f 2 (x), f 3 (x), f 4 (x) }

20. – 20 – Robust Positive Equality Analysis Goal If a variable vf i is a result of eliminating a p-term, then try to assign it a distinct constantQuestion Can we always assign the vf i variables for any p-term a distinct value? Not always Can we compute the set of p-terms that maximizes the number of vf i variables that can be assigned distinct values? In general, NP-complete

21. – 21 – Outline Robust positive equality “Robust” maximal diversity theorem Exploiting robust positive equality Obstacles SolutionsResults Related work

22. – 22 – Robust Maximal Diversity For an interpretation I A p-term f(T) is called is g-arg-distinct, if there is no g-term f(U), such that I [ T ] = I [ U ]. An interpretation I is robust maximally diverse if: For every g-arg-distinct p-term f(T 1 ), 1. I [ f(T 1 ) = f(T 2 ) ] iff I [ T 1 =T 2 ] 2. I [ f(T) ]  I [ g(U) ], for any other function symbol g where f(T 1 ), f(T 2 ), g(U) are terms in the formula

23. – 23 – Equals non f term Example I = {}{} I = { x, f 2 (x), f 4 (x) }, { f(x), f 3 (x) }  (f(x)=x)  (f(f(f(f(x)))) = f(f(f((x))) x =   f f = f f G-term P-term Non robust-maximally diverse interpretation g-arg-distinct For an interpretation I A p-term f(T) is called is g- arg-distinct, if there is no g- term f(U), such that I [ T ] = I [ U ]. An interpretation I is robust maximally diverse if: For every g-arg-distinct p-term f(T 1 ), 1. I [ f(T 1 ) = f(T 2 ) ] iff I [ T 1 =T 2 ] 2. I [ f(T) ]  I [ g(U) ], for any other function symbol g where f(T 1 ), f(T 2 ), g(U) are terms in the formula

24. – 24 – Robust Maximal Diversity Theorem Generalization of positive equality Any robust-maximally diverse interpretation is a maximally diverse interpretations The subset inclusion can be properConsequence Fewer interpretations to consider to check validity Theorem Formula valid if and only if true under all robust maximally diverse interpretations

25. – 25 – Exploiting Robust Positive Equality By Robust maximal diversity theorem Assign a distinct constant to vf i, when i > l f( x i ) Value of vf i = Value of f( x i ) x i x 1,…, x i-1 when x i does not equal { x 1,…, x i-1 } f( x i ) g-arg-distinct i.e. when f( x i ) is g-arg-distinct Function applications f( x 1 ),…, f( x n ) Introduce variables vf 1, …, vf n during elimination f( x 1 ),…, f( x l ),…, f( x i ),…,f( x n ) f Contains all the g-terms for f

26. – 26 – What we need Eliminate the g-terms as early as possible Constrained by the sub-expression ordering e.g. f(x) has to be eliminated before eliminating f(f (x)) Need the best topological order Respects the sub-expression orderings Maximizes the number of vf variables that can be assigned distinct constant value Need to define this objective function precisely

27. – 27 – Function elimination and topological order Requires a topological order on the terms Respects the sub- expression order Eliminate functions from sub-terms first Example order x, f(x), f 2 (x), f 3 (x), f 4 (x) Only order for this example x =    (f(x)=x)  (f(f(f(f(x)))) = f(f(f((x))) f f f = f

28. – 28 – Function elimination and topological order x  f f f = f  (f(f(x))=x)  (f(f(f(f(x)))) = f(f(f((x))) =  Always precedes the g-term f 2 (x) vf variables for every p- term can’t be assigned distinct values vf variables for every p- term can’t be assigned distinct values P-terms that are subterms of a g-term with the same function. Example order x, f(x), f 2 (x), f 3 (x), f 4 (x) Only order for this example

29. – 29 – Topological ordering and the p-terms Topological order < Pos < (f) Set of p-terms of f which do not precede any g- terms of f in < Pos < = f Pos < (f) Pos < =  f Pos < (f)

30. – 30 – Topological ordering: Example 1 Topological order < Pos < (f) Set of p-terms of f which do not precede any g- terms of f in < Pos < = f Pos < (f) Pos < =  f Pos < (f) x =    (f(x)=x)  (f(f(f(f(x)))) = f(f(f((x))) f f f = f + + + Example x< f(x) < f 2 (x) < f 3 (x) < f 4 (x) Pos < = { f 2 (x), f 3 (x), f 4 (x) }

31. – 31 – Topological ordering Property Pos < The vf i variables which results when eliminating terms in Pos < can be assigned a distinct constant valueGoal < Pos < Find the topological order “ < ” that maximizes the size of Pos < Topological order < Pos < (f) Set of p-terms of f which do not precede any g- terms of f in < Pos < = f Pos < (f) Pos < =  f Pos < (f)

32. – 32 – Finding the best topological ordering Example 3 topological orders on terms 1. 1.x<g(x)<f(g(x))<f(x)<g(f(x)) 2. 2.x< f(x)<g(f(x))<g(x)<f(g(x)) 3. 3.x<g(x)< f(x)<g(f(x))<f(g(x))  (f(g(x)) = g(f(x))) = f f g g x  Pos < = {, } Pos < = { x, f(x) } Pos < = {} Pos < = { x, g(x) } Pos < = {} Pos < = { x } Not best for f Not best for g With multiple non-zero arity function symbol Best order may not be best for each symbol

33. – 33 – Obtaining best topological order Complexity NP-complete Polynomial when only 1 non-zero arity function symbol Reduction from the maximum independent set problem Greedy heuristic to find a good order Pos < Assign higher priorities to p-terms of functions with greater number of “potential” terms in Pos < Finds the optimal order for most of the examples we have seen so far.

34. – 34 – Sample Results Implemented in UCLID decision procedure With Zchaff SAT-solver Code Validation Benchmarks [Pnueli, Rodeh, Strichman, Siegel CAV’99] example#vars Positive Equality #pvar time Robust Positive Eq #pvar time Speedup Cv22101170.841645.651.55 Cv4438819.75177.132.77 Cv467010>180028100.50>18

35. – 35 – Observations Robust positive equality improves efficiency Useful in practice Small overhead (+5%) over positive equality analysis Efficient implementation can further reduce this overhead Seldom affects total time when translation time to SAT is a small fraction of the overall time

36. – 36 – Related work Pnueli, Rodeh, Strichman & Siegel CAV’99 Removes function applications by Ackermann’s reduction Range allocation for the resultant formula Assigns smaller ranges for g-terms Rodeh & Strichman CAV’01 Uses Bryant, German & Velev’s function elimination method + range allocation Has similarities and differences with our work

37. – 37 – Conclusions Positive Equality Simplifies function-free formula by reducing the number of variables in the formula Robust Positive Equality Generalization of positive equality Improves applicability for more general benchmarks Can be extended for CLU logic T1 < T2 + c [BLS02; Lahiri MS Thesis] Can we generalize it for linear arithmetic + EUF?

38. – 38 – Questions

39. – 39 – Decision Procedure Benchmarking Model Term formula DAG size Prop formula DAG size UCLID time (s) SVC time (s) CVC time (s) Out-of- order executionUnit73536584.83.06.16 19701375518.3102.490.75 39293717961.94257.3 Out of Mem Elf™processor2189421.210.90.25 108544818.41851.6114.46 24671645330.6 > 1 day Out of Mem 455354288111.0 > 1 day Out of Mem Compared against Stanford Validity Checker (SVC) & its successor CVC (which uses Chaff) Decides CLU + real linear arith. + bit-vector arith. UCLID uses Chaff for Boolean SAT UCLID time = translation time + Chaff time

40. – 40 – Impact of Positive Equality ModelTerm formula size UCLID w/ p-eq. (s) UCLID w/o p- eq. (s) Out-of-order execution unit 7354.789.79 197018.2937.71 392961.90149.46 Cache Protocol 18296.2926.50 278216.13165.91 393961.08 > 1 hr DLX pipeline63913.22 1897 Positive equality can be exploited to improve performance

41. – 41 – Exploiting Positive Equality Property P-function symbol f Introduce variables vf 1, …, vf n during elimination Consider only diverse interpretations for variables vf 1, …, vf n vf i  v for any other variable vExample Assuming vf 1  vf 2 : x1x1 x2x2 vf 1 vf 2 TFTF = = iff x 1 =x 2

42. – 42 – ff vf 1 vf 2 Compare: Ackermann’s Method Replacing Application Introduce new domain variable Enforce functional consistency by global constraints Unclear how to generate diverse interpretations x1x1 x2x2 F ==   

43. – 43 – Decision Procedures in Verification Work-horse for many automated verification methodologies Processor and Protocol verification Pipelined processor verification »Burch & Dill CAV’94, Bryant, German & Velev CAV’99,… Out-of-order processor and cache coherence verification »Lahiri, Seshia & Bryant FMCAD’02, Bryant, Lahiri & Seshia CAV’02 Predicate abstraction Software verification »SLAM (MSR), BLAST (Berkeley), MAGIC (CMU),… Protocol verification » Das, Dill & Park CAV’99,

44. – 44 – Decision Procedures for quantifier- free fragment of first-order logic Principal theories Logic of equality with uninterpreted functions f(x) = f(g(y)) Linear arithmetic Difference-bound logic subset ( T 1 < T 2 + c ) Full linear arithmetic Arrays read and write operationsTools SVC/CVC from Stanford (FMCAD ’96, CAV’02, CAV ‘04) UCLID from CMU (CAV’02, CAV’04) ICS from SRI (CAV ’01) Simplify/Verifun from HP (CAV ’03) Zapato from Microsoft (CAV ’04) ……

45. Carnegie Mellon University Revisiting Positive Equality Shuvendu K. Lahiri Randal E. Bryant Amit Goel Muralidhar Talupur

46. – 46 – Conclusions Generalization of Bryant et al’s positive equality analysis Subsumes original positive equality Exploiting robust positive equality in a decision procedure Problems and heuristics Future Work Integrate smaller range-allocation for the g-terms Pnueli et al. CAV’99, Talupur et al. CAV’04

47. – 47 – Positive Equality for EUF xy gg ff = =    (x=y)  (f(g(x)) = f(g(y)) General (g) Functionsx,yPositiveFunctionsf,g Split the set of terms into p-terms Function applications of p- functions g-terms Function applications of g- functions

48. – 48 – Definition P-term Term which never appear in equations that are g- formulasG-term Term which appears at least once in an equation that is a g-formula x =    (f(x)=x)  (f(f(f(f(x)))) = f(f(f((x))) f f f = f p-terms g-terms

49. – 49 – fff x1x1 x2x2 x3x3 vf 1 vf 2 TFTF = = = TFTF vf 3 TFTF Eliminating Function Applications Bryant, German & Velev CAV’99 Replacing Application Introduce new domain variable Nested ITE structure maintains functional consistency

50. – 50 – Robust maximally diverse interpretations P-term h(T 1,…, T n ) If args. do not equal the args. of any g-term h(U 1,…,U n ), then Can only equal other h application terms with equal argumentsProperty Formula valid if and only if true under all robust maximally diverse interpretations I = {} I = { x  0, f(0)  1, f(1)  0,.. }  (f(x)=x)  (f(f(f(f(x)))) = f(f(f((x))) Equals non f term 0 1 0 1 0 x =   f f = f f G-term P-term Non robust-maximally diverse interpretation Args not equal with the g-term

51. – 51 – Heuristic for obtaining topological order Potentially positive terms for a function Potentially positive terms for a function f The p-terms of f that are not sub-terms of any g-term of fSteps  Sort the function symbols by the number of potentially positive terms  For each function f in sorted order: Put all the g-terms of f (and their subterms) in the topological order  Put all the remaining p- terms in the topological order

52. – 52 – Heuristic for obtaining topological order Sort the functions f;g; x Put the g-terms for f x<g(x)<f(g(x)) Put the g-terms for g f(x)<f(f((x))<g(f(f(x))) Put the g-terms for x Already present  (f(g(x)) = g(f(f(x)))) = f f g g  f ++ + Potentially positive terms for a function Potentially positive terms for a function f The p-terms of f that are not sub-terms of any g-term of fSteps  Sort the function symbols by the number of potentially positive terms  For each function f in sorted order: Put all the g-terms of f (and their subterms) in the topological order  Put all the remaining p- terms in the topological order + x x<g(x)<f(g(x))<f(x)<f(f(x))<g(f(f(x))) T < + = {, } T < + = { x, f(x),f(f(x)) }

53. – 53 – Definitions Interpretation I Assigns a value to all the functions appearing in a formula I(f) = function associated with the symbol fEvaluation I [ e ] evaluates e w.r.t. the interpretation I Defined inductively on the structure of e x =    (f(x)=x)  (f(f(f(f(x)))) = f(f(f((x))) f f f = f I = {} I = { x  0, f(0)  1, f(1)  0,.. } 0 1 0 1 0 false true false

54. – 54 – Topological ordering: Example 2 Topological order < T < + (f) Set of p-terms of f which do not precede any g- terms of f in < T < + = f T < + (f) T < + =  f T < + (f) Example x< f(x) < f 2 (x) < f 3 (x) < f 4 (x) T < + = { f 3 (x), f 4 (x) } x  f f f = f  (f(f(x))=x)  (f(f(f(f(x)))) = f(f(f((x))) + + =  Always precedes the g-term f 2 (x)

55. – 55 – Results Implemented in UCLID decision procedure With Zchaff SAT-solver Code Validation Benchmarks [Pnueli, Rodeh, Strichman, Siegel CAV’99] example #var s Positive Equality #pvar time Robust Positive Eq #pvar |T + | time Speedup Cv22101170.84161845.651.55 Cv23101823.06222215.961.44 Cv25101845.93222221.802.10 Cv4438819.7517177.132.77 Cv467010>18002828100.50>18 T + T + = union of the set of potentially positive terms for each function

56. – 56 – Topological ordering: Example 2 Topological order < Pos < (f) Set of p-terms of f which do not precede any g- terms of f in < Pos < = f Pos < (f) Pos < =  f Pos < (f) Example x< f(x) < f 2 (x) < f 3 (x) < f 4 (x) Pos < = { f 3 (x), f 4 (x) } x  f f f = f  (f(f(x))=x)  (f(f(f(f(x)))) = f(f(f((x))) + + =  Always precedes the g-term f 2 (x)

57. – 57 – Finding the best topological ordering With multiple non-zero arity function symbol Best order may not be best for each symbolExample 3 topological orders on terms 1. 1.x<g(x)<f(g(x))<f(x)<g(f(x)) 2. 2.x< f(x)<g(f(x))<g(x)<f(g(x)) 3. 3.x<g(x)< f(x)<g(f(x))<f(g(x))  (f(g(x)) = g(f(x))) = f f g g x  Pos < = {, } Pos < = { x, f(x) } Pos < = {} Pos < = { x, g(x) } Pos < = {} Pos < = { x }

58. – 58 – Relevant papers “Exploiting positive equality in a logic of equality with uninterpreted functions” “Exploiting positive equality in a logic of equality with uninterpreted functions” Bryant, German and Velev, CAV’99 “Revisiting Positive Equality” “Revisiting Positive Equality” Lahiri, Bryant, Goel and Talupur, TACAS’04 Generalization of positive equality

59. – 59 – Maximally Diverse Interpretations P-Function Symbols Equal results only for equal arguments Doesn’t equal application of any other function symbol G-Function Symbols Potentially yield equal results for unequal argumentsProperty Formula valid if and only if true under all maximally diverse interpretations h xy =  =  g g g h TermsEqual? xy Potentially g (x)g (y) Only if x = y g (x)y No

60. – 60 – Robust maximally diverse interpretations For every p-term h(T 1,…, T n ) If args. do not equal the args. of any g-term h(U 1,…,U n ), then Can only equal other h application terms with equal argumentsProperty Formula valid if and only if true under all robust maximally diverse interpretations

61. – 61 – Robust maximally diverse interpretations P-term h(T 1,…, T n ) If args. do not equal the args. of any g-term h(U 1,…,U n ), then Can only equal other h application terms with equal argumentsProperty Formula valid if and only if true under all robust maximally diverse interpretations I = {}{} I = { x, f 2 (x) }, { f(x), f 3 (x) }  (f(x)=x)  (f(f(f(f(x)))) = f(f(f((x))) Equals non f term x =   f f = f f G-term P-term Non robust-maximally diverse interpretation Arg not equal to the arg of g-term of f