1. 1 SAT Genealogy Alexander Nadel, Intel, Haifa, Israel The Technion, Haifa, Israel July, 3 2012

2. Agenda Introduction Early Days of SAT Solving Core SAT Solving Conflict Analysis and Learning Boolean Constraint Propagation Decision Heuristics Restart Strategies Inprocessing Extensions to SAT Incremental SAT Solving under Assumptions Simultaneous Satisfiability (SSAT) Diverse Solutions Generation High-level (group-oriented) MUC Extraction 2

3. Agenda Introduction Early Days of SAT Solving Core SAT Solving Conflict Analysis and Learning Boolean Constraint Propagation Decision Heuristics Restart Strategies Inprocessing Extensions to SAT Incremental SAT Solving under Assumptions Simultaneous Satisfiability (SSAT) Diverse Solutions Generation High-level (group-oriented) MUC Extraction 3 We won’t use implication graphs for explanation, but: Duality between search and resolution

4. What is SAT? Find a variable assignment (AKA solution or model) that satisfies a propositional formula or prove that there are no solutions SAT solvers operate on CNF formulas: Any formula can be reduced to a CNF 4 CNF Formula: clause negative literal positive literal F = ( a + c ) ( b + c ) (a’ + b’ + c’ )

5. SAT: Theory and Practice Theory: SAT is the first known NP-complete problem Stephen Cook, 1971 One can check a solution in polynomial time Can one find a solution in polynomial time? The P=NP question… Practice: Amazingly, nowadays SAT solvers can solve industrial problems having millions of clauses and variables SAT has numerous applications in formal verification, planning, bioinformatics, combinatorics, … 5

6. Approaches to SAT Solving Backtrack search: DFS search for a solution The baseline approach for industrial-strength solvers. In focus today. Look-ahead: BFS search for a solution Helpful for certain classes of formulas Recently, there were attempts of combining it with backtrack search Local search Helpful mostly for randomly generated formulas 6

7. Early Days of SAT Solving Agenda Resolution Backtrack Search 7

8. a + b + g + h’ + f a + b + g + h’ Resolution: a Way to Derive New Valid Clauses Resolution over a pair of clauses with exactly one pivot variable: a variable appearing in different polarities: a + b + c’ + fg + h’ + c + f - The resolvent clause is a logical consequence of the two source clauses Known to be invented by Davis&Putnam, 1960 Had been invented independently by Lowenheim in early 1900’s (as well as the DP algorithm, presented next) According to Chvatal&Szemeredy, 1988 (JACM)

9. DP Algorithm: Davis&Putnam, 1960 9 (a + b)(a + b’)(a’ + c)(a’ + c’)(a + b + c)(b + c’ + f’)(b’ + e) (a + c + e)(c’ + e + f) (a + e + f) (a’ + c)(a’ + c’) (c)(c’)( ) SAT UNSAT (a) Remove the variables one-by-one by resolution over all the clauses containing that variable DP is sound and complete

10. Backtrack Search or DLL: Davis- Logemann-Loveland, 1962 a + b b’ + c b’ + c’ a’ + b

11. Backtrack Search or DLL: Davis- Logemann-Loveland, 1962 a + b b’ + c b’ + c’ a’ + b a’

12. Backtrack Search or DLL: Davis- Logemann-Loveland, 1962 a + b b’ + c b’ + c’ a’ + b a’ Decision level 1 a is the decision variable; a’ is the decision literal

13. Backtrack Search or DLL: Davis- Logemann-Loveland, 1962 a + b b’ + c b’ + c’ a’ + b a’ b’ Decision level 2

14. Backtrack Search or DLL: Davis- Logemann-Loveland, 1962 a + b b’ + c b’ + c’ a’ + b a + b a’ b’ A conflict. A blocking clause – a clause, falsified by the current assignment – is encountered.

15. Backtrack Search or DLL: Davis- Logemann-Loveland, 1962 a + b b’ + c b’ + c’ a’ + b a + b a’ b’ b Backtrack and flip

16. Backtrack Search or DLL: Davis- Logemann-Loveland, 1962 a + b b’ + c b’ + c’ a’ + b a + b b’ + c a’ b’ b c’ Decision level 1 Decision level 2

17. Backtrack Search or DLL: Davis- Logemann-Loveland, 1962 a + b b’ + c b’ + c’ a’ + b a + b b’ + cb’ + c’ a’ b’ b c’c Decision level 1

18. Backtrack Search or DLL: Davis- Logemann-Loveland, 1962 a + b b’ + c b’ + c’ a’ + b a + b b’ + cb’ + c’ a’ b’ b c’c a

19. Backtrack Search or DLL: Davis- Logemann-Loveland, 1962 a + b b’ + c b’ + c’ a’ + b a + b b’ + cb’ + c’ a’ b’ b c’c a b

20. Backtrack Search or DLL: Davis- Logemann-Loveland, 1962 a + b b’ + c b’ + c’ a’ + b a + b b’ + cb’ + c’ b’ + c a’ b’ b c’c a b

21. Backtrack Search or DLL: Davis- Logemann-Loveland, 1962 a + b b’ + c b’ + c’ a’ + b a + b b’ + cb’ + c’ b’ + cb’ + c’ a’ b’ b c’c a b c

22. Backtrack Search or DLL: Davis- Logemann-Loveland, 1962 a + b b’ + c b’ + c’ a’ + b a + b b’ + cb’ + c’ b’ + cb’ + c’ a’ + b a’ b’ b c’c a b c b’

23. Backtrack Search or DLL: Davis- Logemann-Loveland, 1962 a + b b’ + c b’ + c’ a’ + b a + b b’ + cb’ + c’ b’ + cb’ + c’ a’ + b a’ b’ b c’c a b c b’ UNSAT!

24. Core SAT Solving: the Principles DLL could solve problems with <2000 clauses How can modern SAT solvers solve problems with millions of clauses and variables? The major principles: Learning and pruning Block already explored paths Locality and dynamicity Focus the search on the relevant data Well-engineered data structures Extremely fast propagation 24

25. Agenda Introduction Early Days of SAT Solving Core SAT Solving Conflict Analysis and Learning Boolean Constraint Propagation Decision Heuristics Restart Strategies Inprocessing Extensions to SAT Incremental SAT Solving under Assumptions Simultaneous Satisfiability (SSAT) Diverse Solutions Generation High-level (group-oriented) MUC Extraction 25

26. Duality between Basic Backtrack Search and Resolution One can associate a resolution derivation with every invocation of DLL over an unsatisfiable formula

27. Duality between Basic Backtrack Search and Resolution a + b b’ + c b’ + c’ a’ + b

28. Duality between Basic Backtrack Search and Resolution a + b b’ + c b’ + c’ a’ + b a’

29. Duality between Basic Backtrack Search and Resolution a + b b’ + c b’ + c’ a’ + b a + b a’ b’

30. Duality between Basic Backtrack Search and Resolution a + b b’ + c b’ + c’ a’ + b a + b a’ b’ A parent clause P(x) is associated with every flip operation for variable x. It contains: The flipped literal A subset of previously assigned falsified literals The parent clause justifies the flip: its existence proves that the explored subspace has no solutions b

31. Duality between Basic Backtrack Search and Resolution a + b b’ + c b’ + c’ a’ + b a + b b’ + c a’ b’ b c’

32. Duality between Basic Backtrack Search and Resolution a + b b’ + c b’ + c’ a’ + b a + b b’ + c a’ b’ b c’c

33. Duality between Basic Backtrack Search and Resolution a + b b’ + c b’ + c’ a’ + b a + b b’ + cb’ + c’ a’ b’ b c’c

34. Duality between Basic Backtrack Search and Resolution a + b b’ + c b’ + c’ a’ + b b’ a + b b’ + cb’ + c’ a’ b’ b c’c Backtracking over a flipped variable x can be associated with a resolution operation: P = P(x)  P P is to become the parent clause for the upcoming flip P is initialized with the last blocking clause P old P(c) P new

35. Duality between Basic Backtrack Search and Resolution a + b b’ + c b’ + c’ a’ + b a b’ a + b b’ + cb’ + c’ a’ b’ b c’c Backtracking over a flipped variable x can be associated with a resolution operation: P = P(x)  P P is to become the parent clause for the upcoming flip P is initialized with the last blocking clause P new P old P(b)

36. b’ b c’c Duality between Basic Backtrack Search and Resolution a + b b’ + c b’ + c’ a’ + b a b’ a + b b’ + cb’ + c’ a’a  (a) The parent clause P(a) is derived by resolution. The resolution proof  (a) of the parent clause is called parent resolution

37. Duality between Basic Backtrack Search and Resolution a + b b’ + c b’ + c’ a’ + b a’a b b’ b c’c a b’ a + b b’ + cb’ + c’

38. Duality between Basic Backtrack Search and Resolution a + b b’ + c b’ + c’ a’ + b b’ + c a’a b c’ b’ b c’c a b’ a + b b’ + cb’ + c’

39. Duality between Basic Backtrack Search and Resolution a + b b’ + c b’ + c’ a’ + b b’ + c a’a b c’c b’ b c’c a b’ a + b b’ + cb’ + c’ P(c)

40. Duality between Basic Backtrack Search and Resolution a + b b’ + c b’ + c’ a’ + b b’ + cb’ + c’ a’a b c’c b’ b c’c a b’ a + b b’ + cb’ + c’

41. Duality between Basic Backtrack Search and Resolution a + b b’ + c b’ + c’ a’ + b b’ b’ + cb’ + c’ a’a b c’c b’ b c’c a b’ a + b b’ + cb’ + c’ P old P(c) P new

42. Duality between Basic Backtrack Search and Resolution a + b b’ + c b’ + c’ a’ + b b’ b’ + cb’ + c’ a’a b c’c b’ b c’c a b’ a + b b’ + cb’ + c’  (b)

43. Duality between Basic Backtrack Search and Resolution a + b b’ + c b’ + c’ a’ + b b’ b’ + cb’ + c’ a’ + b a’a b c’c b’ b c’c a b’ a + b b’ + cb’ + c’

44. Duality between Basic Backtrack Search and Resolution a + b b’ + c b’ + c’ a’ + b a’ b’ b’ + cb’ + c’ a’ + b a’a b c’c b’ b c’c a b’ a + b b’ + cb’ + c’ P old P(b) P new

45. Duality between Basic Backtrack Search and Resolution a + b b’ + c b’ + c’ a’ + b a’ b’ b’ + cb’ + c’ a’ + b a’a b c’c b’ b c’c a b’ a + b b’ + cb’ + c’ P old P(a) P new

46. Duality between Basic Backtrack Search and Resolution a + b b’ + c b’ + c’ a’ + b a’ b’ b’ + cb’ + c’ a’ + b a’a b c’c b’ b c’c a b’ a + b b’ + cb’ + c’

47. Duality between Basic Backtrack Search and Resolution a’ b’ b’ + cb’ + c’ a’ + b a’a b c’c b’ b c’c a b’ a + b b’ + cb’ + c’  The final trace of DLL is both a decision tree (top-down view) and a resolution refutation (bottom-up view)  Variables associated with the edges are both decision variables in the tree and pivot variables for the resolution  A forest of parent resolutions is maintained  The forest converges to one resolution refutation in the end (for an UNSAT formula)

48. Conflict Clause Recording a’ b’ b’ + cb’ + c’ a’ + b a’a b c’c b’ b c’c a b’ a + b b’ + cb’ + c’ The idea: update the instance with conflict clauses, that is some of the clauses generated by resolution Introduced in SAT by Bayardo&Schrag, 1997 (rel_sat)

49. Conflict Clause Recording a’ b’ b’ + cb’ + c’ a’ + b a’a b c’c b’ b c’c a b’ a + b b’ + cb’ + c’ Assume the brown clause below was recorded

50. Conflict Clause Recording a’ b’ b’ + cb’ + c’ a’ + b a’a b c’c b’ b c’c a b’ a + b b’ + cb’ + c’ Assume the brown clause below was recorded The violet part would not have been explored It is redundant

51. Conflict Clause Recording a’ a’ + b a’a b b’ b c’c a b’ a + b b’ + cb’ + c’ Assume the brown clause below was recorded The violet part would not have been explored It is redundant

52. Conflict Clause Recording Most of the modern solvers record every non-trivial parent clause (since Chaff) : recorded : not recorded a’ b’ c’cd’df’fg’g be’e a

53. Enhancing CCR: Local Conflict Clause Recording The parent-based scheme is asymmetric w.r.t polarity selection a’ b’ c’cd’df’fg’g be’e a

54. Enhancing CCR: Local Conflict Clause Recording The parent-based scheme is asymmetric w.r.t polarity selection Solution: record an additional local conflict clause: a would-be conflict clause if the last polarity selection was flipped Dershowitz&Hanna&Nadel, 2007 (Eureka) : local conflict clause a’ b’ c’cd’df’fg’g be’e a

55. Managing Conflict Clauses Keeping too many clauses slows down the solver Deleting irrelevant clauses is very important. Some of the strategies: Size-based: remove too long clauses Marques-Silva&Sakallah, 1996 (GRASP) Age-based: remove clauses that weren’t used for BCP Goldberg&Novikov, 2002 (Berkmin) Locality-based (glue): remove clauses, whose literals are assigned far away in the search tree Audemard&Simon, 2009 (Glucose) 55

56. Modern Conflict Analysis Next, we present the following two techniques, commonly used in modern SAT solvers: Non-chronological backtracking (NCB) GRASP 1UIP scheme GRASP& Chaff Both techniques prune the search tree and the associated forest of parent resolutions

57. Non-Chronological Backtracking (NCB) b’ b c’c a + b b’ + c b’ + c’ a’ + b a b’ a + b b’ + cb’ + c’ a’ … d’  NCB is an additional pruning operation before flipping: eliminate all the decision levels adjacent to the decision level of the flipped literal, so that the parent clause is still falsified e  (e) e’ Assume we are about to flip a

58. Non-Chronological Backtracking (NCB) b’ b c’c a + b b’ + c b’ + c’ a’ + b a b’ a + b b’ + cb’ + c’ a’ … d’  NCB is an additional pruning operation before flipping: eliminate all the decision levels adjacent to the decision level of the flipped literal, so that the parent clause is still falsified e  (e) e’ Assume we are about to flip a Eliminate irrelevant decision levels

59. Non-Chronological Backtracking (NCB) b’ b c’c a + b b’ + c b’ + c’ a’ + b a b’ a + b b’ + cb’ + c’ a’ …  NCB is an additional pruning operation before flipping: eliminate all the decision levels adjacent to the decision level of the flipped literal, so that the parent clause is still falsified Assume we are about to flip a Eliminate irrelevant decision levels Flip a

60. 1UIP Scheme

61.  1UIP scheme consists of:  A stopping condition for backtracking: stop whenever P contains one variable of the last decision level, called the 1UIP variable

62. 1UIP Scheme  1UIP scheme consists of:  A stopping condition for backtracking: stop whenever P contains one variable of the last decision level, called the 1UIP variable a + b b’ + c b’ + c’ a’ + b b’ a + b b’ + cb’ + c’ a’ b’ b c’c P

63. 1UIP Scheme  1UIP scheme consists of:  A stopping condition for backtracking: stop whenever P contains one variable of the last decision level, called the 1UIP variable  A rewriting operation: consider the 1UIP variable as a decision variable and P as its parent clause a + b b’ + c b’ + c’ a’ + b b’ a + b b’ + cb’ + c’ a’ b’ b c’c P

64. 1UIP Scheme  1UIP scheme consists of:  A stopping condition for backtracking: stop whenever P contains one variable of the last decision level, called the 1UIP variable  A rewriting operation: consider the 1UIP variable as a decision variable and P as its parent clause a + b b’ + c b’ + c’ a’ + b b’ a + b b’ + cb’ + c’ a’ b’ b c’c P

65. 1UIP Scheme  1UIP scheme consists of:  A stopping condition for backtracking: stop whenever P contains one variable of the last decision level, called the 1UIP variable  A rewriting operation: consider the 1UIP variable as a decision variable and P as its parent clause a + b b’ + c b’ + c’ a’ + b b’ a + b b’ + cb’ + c’ a’ b’ b c’c

66. 1UIP Scheme  1UIP scheme consists of:  A stopping condition for backtracking: stop whenever P contains one variable of the last decision level, called the 1UIP variable  A rewriting operation: consider the 1UIP variable as a decision variable and P as its parent clause  A pruning technique: eliminate all the disconnected variables of the last decision level (along with their parent resolutions) a + b b’ + c b’ + c’ a’ + b b’ a + b b’ + cb’ + c’ a’ b’ b c’c

67. 1UIP Scheme  1UIP scheme consists of:  A stopping condition for backtracking: stop whenever P contains one variable of the last decision level, called the 1UIP variable  A rewriting operation: consider the 1UIP variable as a decision variable and P as its parent clause  A pruning technique: eliminate all the disconnected variables of the last decision level (along with their parent resolutions) a + b b’ + c b’ + c’ a’ + b b’ b’ + cb’ + c’ b c’c b’

68. Agenda Introduction Early Days of SAT Solving Core SAT Solving Conflict Analysis and Learning Boolean Constraint Propagation Decision Heuristics Restart Strategies Inprocessing Extensions to SAT Incremental SAT Solving under Assumptions Simultaneous Satisfiability (SSAT) Diverse Solutions Generation High-level (group-oriented) MUC Extraction 68

69. The unit clause rule A clause is unit if all of its literals but one are assigned to 0. The remaining literal is unassigned, e.g.: Boolean Constraint Propagation (BCP) Pick unassigned variables of unit clauses as decisions whenever possible 80-90% of running time of modern SAT solvers is spent in BCP Introduced already in the original DLL a = 0, b = 1, c is unassigned a + b’ +  c Boolean Constraint Propagation 69

70. Data Structures for Efficient BCP Naïve: for each clause hold pointers to all its literals How to minimize the number of clause visits? When can a clause become unit? All literals in a clause but one are assigned to 0 For an N-literal clause, this can only occur after N-1 of the literals have been assigned to 0 So, theoretically, one could completely ignore the first N-2 assignments to this clause. The solution: one picks two literals in each clause to watch and thus can ignore any assignments to the other literals in the clause. Introduced by Zhang, 1997 (SATO solver); enhanced by Moskewicz& Madigan&Zhao&Zhang&Malik, 2001 (Chaff) 70

71. Watched Lists : Example abcdefgh WW 71

72. Watched Lists : Example abcdefgh WW 72 a’

73. Watched Lists : Example abcdefgh W W The clause is visited The corresponding watch moves to any unassigned literal No pointers to the previously visited literals are saved 73 a’

74. Watched Lists : Example abcdefgh W W 74 a’ c’

75. Watched Lists : Example abcdefgh W W The clause is not visited! 75 a’ c’

76. Watched Lists : Example abcdefgh W W 76 a’ c’ g’ e’

77. Watched Lists : Example abcdefgh W W The clause is not visited! 77 a’ c’ g’ e’

78. Watched Lists : Example abcdefgh W W 78 a’ c’ g’ e’ h’

79. Watched Lists : Example abcdefgh WW The clause is visited The corresponding watch moves to any unassigned literal No pointers to the previously visited literals are saved 79 a’ c’ e’ g’ h’

80. Watched Lists : Example abcdefgh WW 80 a’ c’ e’ g’ h’ f’

81. Watched Lists : Example abcdefgh WW 81 a’ c’ e’ g’ h’ f’

82. Watched Lists : Example abcdefgh WW 82 a’ c’ e’ g’ h’ f’ b’

83. Watched Lists : Example abcdefgh WW The watched literal b is visited. It is identified that the clause became unit! 83 a’ c’ e’ g’ h’ f’ b’

84. Watched Lists : Example abcdefgh W b is unassigned : the watches do not move No need to visit the clause during backtracking! W 84 a’ c’ e’ g’ h’ f’  Backtrack b’

85. Watched Lists : Example f is unassigned : the watches do not move  Backtrack abcdefgh WW 85 a’ c’ e’ g’ h’ f’ b’

86. Watched Lists : Example a’ c’ e’ g’ h’ When all the literals are unassigned, the watches pointers do not get back to their initial positions f’  Backtrack abcdefgh WW 86 b’

87. Watched Lists : Caching Chu&Harwood&Stuckey, 2008 Divide the clauses into various cache levels to improve cache performance Most of the modern solvers put one literal of each clause in the WL Special data structures for clauses of length 2 and 3 87

88. Agenda Introduction Early Days of SAT Solving Core SAT Solving Conflict Analysis and Learning Boolean Constraint Propagation Decision Heuristics Restart Strategies Inprocessing Extensions to SAT Incremental SAT Solving under Assumptions Simultaneous Satisfiability (SSAT) Diverse Solutions Generation High-level (group-oriented) MUC Extraction 88

89. Decision Heuristics Which literal should be chosen at each decision point? Critical for performance!

90. Old-Days’ Static Decision Heuristics Go over all clauses that are not satisfied Compute some function f(A) for each literal— based on frequency Choose literal with maximal f(A)

91. Variable-based Dynamic Heuristics: VSIDS VSIDS was the first dynamic heuristic (Chaff) Each literal is associated with a counter Initialized to number of occurrences in input Counter is increased when the literal participates in a conflict clause Occasionally, counters are halved Literal with the maximal counter is chosen Breakthrough compared to static heuristics: Dynamic: focuses search on recently used variables and clauses Extremely low overhead

92. Enhancements to VSIDS Adjusting the scope: increase the scores for every literal in the newly generated parent resolution (Berkmin) Additional dynamicity: multiply scores by 95% after each conflict, rather than occasionally halve the scores Eén&Sörensson, 2003 (Minisat) 92

93. The Clause-Based Heuristic (CBH) The idea: use relevant clauses for guiding the decision heuristic The Clause-Based Heuristic or CBH (Eureka) All the clauses (both initial and conflict clauses) are organized in a list The next variable is chosen from the top-most unsatisfied clause After a conflict: All the clauses that participate in the newly derived parent resolution are moved to the top, then The conflict clause is placed at the top Partial clause-based heuristics: Berkmin, HaifaSAT

94. CBH: More CBH is even more dynamic than VSIDS: prefers variables from very recent conflicts CBH tends to pick interrelated variables: Variables whose joint assignment increases the chances of: Satisfying clauses in satisfiable branches Quickly reaching conflicts in unsatisfiable branches Variables appearing in the same clause are interrelated: Picking variables from the same clause, results in either that:  the clause becomes satisfied, or  there’s a contradiction 94

95. Polarity Selection Phase Saving: Strichman, 2000; Pipatsrisawat&Darwiche, 2007 (RSAT) Assign a new decision variable the last polarity it was assigned: dynamicity rules again 95

96. Decision Heuristics: the Current Status Everybody uses phase saving Most of the SAT solvers use VSIDS Intel’s Eureka uses CBH for most of the instances and VSIDS for tiny instances only We plan to compare VSIDS and CBH thoroughly in our new solver Fiver 96

97. Core SAT Solving: the Major Enhancements to DLL Boolean Constraint Propagation Conflict Analysis and Learning Decision Heuristics Restart Strategies Pre- and Inter- Processing 97 The slides on restarts are based on Vadim Ryvchin’s SAT’08 presentation

98. Agenda Introduction Early Days of SAT Solving Core SAT Solving Conflict Analysis and Learning Boolean Constraint Propagation Decision Heuristics Restart Strategies Inprocessing Extensions to SAT Incremental SAT Solving under Assumptions Simultaneous Satisfiability (SSAT) Diverse Solutions Generation High-level (group-oriented) MUC Extraction 98

99. 99 Restarts Restarts: the solver backtracks to decision level 0, when certain criteria are met crucial impact on performance Motivation: Dynamicity: refocus the search on relevant data Variables identified as important will be pick first by the decision heuristic after the restart Avoid spending too much time in ‘bad’ branches

100. 100 Restart Criteria Restart after a certain number of conflicts has been encountered either: Since the previous restart: global Gomes&Selman&Kautz, 1998 Higher than a certain decision level: local Ryvchin&Strichman, 2008 Next: methods to calculate the threshold on the number of conflicts Holds for both global and local schemes

101. 101 Restarts Strategies 1. Arithmetic (or fixed) series. Parameters: x, y. Init(t) = x Next(t)=t+y

102. 102 Restarts Strategies (cont.) 2. Luby et al. series. Parameter: x. Init(t) = x Next(t) = t i *x Ruan&Horvitz&Kautz, 2003 t i =1 1 2 1 1 2 4 1 1 2 1 1 2 4 8 1 1 2 1 1 2 4 1 1 2 1 1 2 4 8 16 1 1 2 1 1 2 4 1 1 2 1 1 2 4 8 …

103. 103 Restarts Strategies (cont.) 3. Inner-Outer Geometric series. Parameters: x, y, z. Init(t) = x if (t*y < z) Next(t) = t*y else Next(t) = x Next(z) = z*y Armin Biere, 2007 (Picosat)

104. Agenda Introduction Early Days of SAT Solving Core SAT Solving Conflict Analysis and Learning Boolean Constraint Propagation Decision Heuristics Restart Strategies Inprocessing Extensions to SAT Incremental SAT Solving under Assumptions Simultaneous Satisfiability (SSAT) Diverse Solutions Generation High-level (group-oriented) MUC Extraction 104

105. Preprocessing and Inprocessing The idea: Simplify the formula prior (pre-) and during (in-) the search History: Freeman, 1995 (POSIT): first mentioning of preprocessing in the context of SAT Eén&Biere, 2005 (SatELite): a commonly used efficient preprocessing procedure Heule&Järvisalo&Biere (2010-2012): a series of papers on inprocessing Used in the current state-of-the-art solvers Lingeling and CryptoMinisat Nadel&Ryvchin&Strichman (2012): apply SatELite in incremental SAT solving 105

106. Inprocessing Techniques SatELite: Subsumption: remove clause (C+D) if (C) exists Self-subsuming resolution: replace (D+l’) by (D), if (C+l) exists, such that C  D Variable elimination: apply DP for variables, whose elimination does not increase the number of clauses Example: (a+b)(a+b’)(a’+c)(a’+c’)  (a)(a’+c)(a’+c’) Example of other techniques: Failed literal elimination with BCP: Repeat for a certain subset of literals on decision level 0:  Propagate a literal l with BCP.  If a conflict emerges, l must be 0  the formula can be simplified 106

107. Agenda Introduction Early Days of SAT Solving Core SAT Solving Conflict Analysis and Learning Boolean Constraint Propagation Decision Heuristics Restart Strategies Inprocessing Extensions to SAT Incremental SAT Solving under Assumptions Simultaneous Satisfiability (SSAT) Diverse Solutions Generation High-level (group-oriented) MUC Extraction 107

108. Extensions to SAT Nowadays, SAT solving is much more than finding one solution to a given problem Extensions to SAT: Incremental SAT under assumptions Simultaneous SAT (SSAT): SAT over multiple properties at once Diverse solution generation Minimal Unsatisfiable Core (MUC) extraction Push/pop support Model minimization ALL-SAT XOR clauses support ISSAT: assumptions are implications … 108

109. Agenda Introduction Early Days of SAT Solving Core SAT Solving Conflict Analysis and Learning Boolean Constraint Propagation Decision Heuristics Restart Strategies Inprocessing Extensions to SAT Incremental SAT Solving under Assumptions Simultaneous Satisfiability (SSAT) Diverse Solutions Generation High-level (group-oriented) MUC Extraction 109

110. Incremental SAT Solving under Assumptions The challenge: speed-up solving of related SAT instances by enabling re-use of relevant data Incremental SAT solving has numerous applications Next, we review a prominent application in Formal Verification of Hardware 110

111. Reasoning about Circuit Properties with SAT-based Bounded Model Checking (BMC) BMC: given a circuit and a property, does the property holds for the first n cycles? Unroll: generate a combinational instantiation of the circuit for each cycle Run a SAT solver for each cycle over: The translation of unrolled circuit to CNF The negation of the property at that cycle The property holds for n cycles iff all the SAT solver invocations return UNSAT 111

112. BMC Example a b ch g The property: b’  h’

113. BMC Example: Cycle 0 a b h g cici A user-given initial value a b ch g The property: b’  h’

114. BMC Example: Cycle 0 a b h g cici h + g’ + c i ’ h’ + g h’ + c i g + a’ + b’ g’ + a g’ + b b’ h The negation of the property b’  h’: a b ch g UNSAT! The property: b’  h’

115. BMC Example: Cycle 1 a b h g cici a b ch g bxbx hxhx cxcx axax gxgx The property: b’  h’

116. BMC Example: Cycle 1 a b h g cici h + g’ + c i ’ h’ + g h’ + c i g + a’ + b’ g’ + a g’ + b bx’hxbx’hx The negation of the property b x ’  h x ’: a b ch g bxbx hxhx cxcx c x + h’ c x ’ + h axax gxgx g x + a x ’ + b x ’ g x ’ + a x g x ’ + b x h x + g x ’ + c x ’ h x ’ + g x h x ’ + c x UNSAT! The property: b’  h’

117. Re-Using Relevant Information from Previous Cycles 117 The property: b’  h’ a b h g cici bxbx hxhx cxcx h + g’ + c i ’ h’ + g h’ + c i g + a’ + b’ g’ + a g’ + b b’ h bx’hxbx’hx c x + h’ c x ’ + h g x + a x ’ + b x ’ g x ’ + a x g x ’ + b x h x + g x ’ + c x ’ h x ’ + g x h x ’ + c x C 0 : cycle 0C 1 : cycle 1 S 0 : cycle 0-specific S 1 : cycle 1-specific C 0 and C 1 hold globally S 0 and S 1 hold solely for a particular cycle

118. Pervasive Clause Learning; Marques- Silva&Sakallah, 1997 (GRASP); Strichman, 2001 118 Cycle 0: create a CNF instance C 0  S 0 and solve it Let C 0 * be the set of pervasive conflict clauses, that is conflict clauses that depend only on C 0 Cycle 1: create a CNF instance C 0  C 1  S 1  C 0 * and solve it h + g’ + c i ’ h’ + g h’ + c i g + a’ + b’ g’ + a g’ + b b’ h bx’hxbx’hx c x + h’ c x ’ + h g x + a x ’ + b x ’ g x ’ + a x g x ’ + b x h x + g x ’ + c x ’ h x ’ + g x h x ’ + c x C 0 : cycle 0C 1 : cycle 1 S 0 : cycle 0-specific S 1 : cycle 1-specific

119. 119 Cycle 0: create a CNF instance C 0  S 0 and solve it Let C 0 * be the set of pervasive conflict clauses, that is conflict clauses that depend only on C 0 Cycle 1: create a CNF instance C 0  C 1  S 1  C 0 * and solve it a + h’ g h + g’ + c i ’ h’ + g h’ + c i g + a’ + b’ g’ + a g’ + b b’ h bx’hxbx’hx c x + h’ c x ’ + h g x + a x ’ + b x ’ g x ’ + a x g x ’ + b x h x + g x ’ + c x ’ h x ’ + g x h x ’ + c x C 0 : cycle 0C 1 : cycle 1 S 0 : cycle 0-specific S 1 : cycle 1-specific C0*C0* Pervasive Clause Learning; Marques- Silva&Sakallah, 1997 (GRASP); Strichman, 2001

120. Incremental SAT Solving under Assumptions; Eén&Sörensson, 2003 (Minisat) 120 Cycle 0: create a CNF instance C 0 and solve it under the assumptions S 0 S 0 clauses are not part of the instance, instead: The literals of S 0 are used as the first decision, or assumptions The solver stops, whenever one of the assumptions must be flipped Cycle 1: add the clauses C 1 to the same instance and solve under the assumptions S 1 h + g’ + c i ’ h’ + g h’ + c i g + a’ + b’ g’ + a g’ + b b’ h bx’hxbx’hx c x + h’ c x ’ + h g x + a x ’ + b x ’ g x ’ + a x g x ’ + b x h x + g x ’ + c x ’ h x ’ + g x h x ’ + c x C 0 : cycle 0C 1 : cycle 1 S 0 : cycle 0-specific S 1 : cycle 1-specific

121. Incremental SAT Solving: More Minisat’s method is the state-of-the-art Advantages: Re-uses a single solver instance: heuristics are incremental All the clauses are re-used GRASP’s method advantage Assumptions are unit clauses: preprocessing can use them to simplify the formula Incremental SAT solving was not compatible with preprocessing Nadel&Ryvchin&Strichman 2012: Make incremental SAT solving compatible with SatELite Show a way to treat assumptions efficiently 121

122. Agenda Introduction Early Days of SAT Solving Core SAT Solving Conflict Analysis and Learning Boolean Constraint Propagation Decision Heuristics Restart Strategies Inprocessing Extensions to SAT Incremental SAT Solving under Assumptions Simultaneous Satisfiability (SSAT) Diverse Solutions Generation High-level (group-oriented) MUC Extraction 122

123. Simultaneous SAT (SSAT) A SAT-based algorithm to efficiently solve chunks of related properties in one SAT solver invocation For example, one can solve multiple properties during BMC Khasidashvili&Nadel&Palti&Hanna, 2005 Khasidashvili&Nadel, 2011 123

124. p1p1 p1p1 p2p2 p2p2 C2C2 C2C2 C1C1 C1C1 Example: Solve Both p 1 and p 2

125. Incremental SAT-based Approach p1p1 p1p1 p2p2 p2p2 C2C2 C2C2 C1C1 C1C1 Translate C 1 to CNF formula F Solve F under the assumption p 1 ’ Update F with clause projection of C 2 \C 1 Solve F under the assumption p 2 ’

126. SSAT Approach p1p1 p1p1 p2p2 p2p2 C2C2 C2C2 C1C1 C1C1 Translate both C 1 and C 2 to CNF formula F Find the status of both p 1 and p 2 in the same invocation of the SAT solver

127. Advantages of SSAT approach to Incremental SAT-based Approach Looks at all the properties at once One solution can falsify more than one property May find conflict clauses (lemmas) relevant for solving many POs

128. SSAT: the Algorithm Interface Input A combinational formula F (in CNF) A list of proof objectives (POs) p 1,p 2,…,p n Output Each p i is either falsifiable  A model to F, such that p i = 0, exists (F  p i ’ is SAT) valid  p i always holds, given F (F  p i ’ is UNSAT) 128

129. SSAT Algorithm Interface Example F = (a + b)  c’  a’ POs: a, b, c, a’, b’, c’ a is falsifiable: a = 0; b = 1; c = 0 is the model b is valid: there is no model to F, where b = 0 In another words, (a + b)  c’  a’  b’ is UNSAT c is falsifiable: a = 0; b = 1; c = 0 is the model a’ is valid: no model to F where a = 1 b’ is falsifiable with a = 0; b = 1; c = 0 c’ is valid: no model to F where c = 1 Both l and l’ may be falsifiable Example: F = a + b; PO: a 129

130. Basic SSAT Algorithm SSAT(F; P={p 1,p 2,…,p n }) While (P is non-empty) Pick any s  P Solve F under the assumption s’ If satisfiable by a satisfying assignment   T:={s  other POs in P falsified by  }  Return to the user that the POs T are falsifiable  P := P \ T If unsatisfiable  Return that s is valid  P := P \ {p} Initialized with clause projection of the union of cones of all the properties

131. SSAT: More How to boost SSAT Take further advantage of reasoning about all the POs at once Pick all the POs as decision variables and assign them 0 Fairness: rotate unsolved POs Set an internal time threshold for an attempt to solve one PO When the threshold expires:  Move the unresolved PO to the end of unsolved POs list  Switch to another PO SSAT is widely used at Intel Applied as the core reasoning engine for simultaneous model checking algorithms we developed

132. Agenda Introduction Early Days of SAT Solving Core SAT Solving Conflict Analysis and Learning Boolean Constraint Propagation Decision Heuristics Restart Strategies Inprocessing Extensions to SAT Incremental SAT Solving under Assumptions Simultaneous Satisfiability (SSAT) Diverse Solutions Generation High-level (group-oriented) MUC Extraction 132

133. DiversekSet: Generating Diverse Solutions DiversekSet in SAT: generate a user-given number of diverse solutions, given a CNF formula Nadel, 2011 The problem has multiple applications at Intel 133

134. New Initial states initial states deep bugs Max FV bound Application: Semi-formal FPV

135. Multi-Threaded Search to Enhance Coverage Choosing a single path through waypoints may miss the bug Must search along multiple diverse paths calculated:

136. Diversification Quality as the Average Hamming Distance Quality: the average Hamming distance between the solutions, normalized to [0…1] a b c  1 0 0 0  2 1 1 0  3 0 1 1  4 1 0 0 Hamming distances matrix 2

137. Diversification Quality as the Average Hamming Distance Quality: the average Hamming distance between the solutions, normalized to [0…1] a b c  1 0 0 0  2 1 1 0  3 0 1 1  4 1 0 0 Hamming distances matrix 22

138. Diversification Quality as the Average Hamming Distance Quality: the average Hamming distance between the solutions, normalized to [0…1] a b c  1 0 0 0  2 1 1 0  3 0 1 1  4 1 0 0 Hamming distances matrix 221

139. Diversification Quality as the Average Hamming Distance Quality: the average Hamming distance between the solutions, normalized to [0…1] a b c  1 0 0 0  2 1 1 0  3 0 1 1  4 1 0 0 Hamming distances matrix 221 2

140. Diversification Quality as the Average Hamming Distance Quality: the average Hamming distance between the solutions, normalized to [0…1] a b c  1 0 0 0  2 1 1 0  3 0 1 1  4 1 0 0 Hamming distances matrix 221 12

141. Diversification Quality as the Average Hamming Distance Quality: the average Hamming distance between the solutions, normalized to [0…1] a b c  1 0 0 0  2 1 1 0  3 0 1 1  4 1 0 0 Hamming distances matrix 221 12 3

142. Diversification Quality as the Average Hamming Distance Quality: the average Hamming distance between the solutions, normalized to [0…1] a b c  1 0 0 0  2 1 1 0  3 0 1 1  4 1 0 0 Hamming distances matrix 221 12 3 VariablesSolutions Hamming Distance

143. Diversification Quality as the Average Hamming Distance Quality: the average Hamming distance between the solutions, normalized to [0…1] a b c  1 0 0 0  2 1 1 0  3 0 1 1  4 1 0 0 Hamming distances matrix 221 12 3

144. Algorithms for DiversekSet in SAT in a Glance The idea: Adapt a modern CDCL SAT solver for DiversekSet Make minimal changes to remain efficient Compact algorithms: Invoke the SAT solver once to generate all the solutions Restart after a solution is generated Modify the polarity and variable selection heuristics for generating diverse solutions

145. Algorithms for DiversekSet in SAT in a Glance Cont. Polarity-based algorithms: Change solely the polarity selection heuristic pRand: pick the polarity randomly pGuide: pick the polarity so as to improve the diversification quality Balance the number of 0’s and 1’s assigned to a variable by picking  {0,1} when variable was assigned  ’ more times pGuide outperforms pRand in terms of both diversification quality and performance Quality can be improved further by taking BCP into account and adapting the variable ordering

146. Agenda Introduction Early Days of SAT Solving Core SAT Solving Conflict Analysis and Learning Boolean Constraint Propagation Decision Heuristics Restart Strategies Inprocessing Extensions to SAT Incremental SAT Solving under Assumptions Simultaneous Satisfiability (SSAT) Diverse Solutions Generation High-level (group-oriented) MUC Extraction 146

147. Unsatisfiable Core Extraction An unsatisfiable core is an unsatisfiable subset of an unsatisfiable set of constraints An unsatisfiable core is minimal if removal of any constraint makes it satisfiable (local minima) Has numerous applications

148. Example Application: Proof-based Abstraction Refinement for Model Checking; McMillan et al.,’03; Gupta et al.,’03 No Bug Valid Model Check A BMC(M,P,k) Cex C at depth k Bug No A  A  latches/gates in the UNSAT core of BMC(M,P,k) Inputs: model M, property P Output: does P hold under M? Abstract model A  { } Spurious? The UNSAT core is used for refinement The UNSAT core is required in terms of latches/gates Yes Turn latches/ gates into free inputs

149. Example Application 2: Assumption Minimization for Compositional Formal Equivalence Checking (FEC); Cohen et al.,’10 FEC verifies the equivalence between the design (RTL) and its implementation (schematics). The whole design is too large to be verified at once. FEC is done on small sub-blocks, restricted with assumptions. Assumptions required for the proof of equivalence of sub- blocks must be proved relative to the driving logic. MUC extraction in terms of assumptions is vital for feasibility. Inputs Outputs Assumption Assertion

150. Traditionally, a Clause-Level UC Extractor is the Workhorse Clause-level UC extraction: given a CNF formula, extract an unsatisfiable subset of its clauses F = ( a + b ) ( b’ + c ) (c’ ) (a’ + c ) ( b + c ) ( a + b + c’ ) Dozens of papers on clause-level UC extraction since 2002

151. Traditional UC Extraction for Practical Needs: the Input An interesting constraint The remainder (the rest of the formula) The user is interested in a MUC in terms of these constraints

152. Traditional UC Extraction: Example Input 1 An unrolled latch The rest of the unrolled circuit Proof-based abstraction refinement

153. Traditional UC Extraction: Example Input 1 An assumption Equivalence between sub-block RTL and implementation Assumption minimization for FEV

154. Traditional UC Extraction: Stage 1: Translate to Clauses An interesting constraint The remainder (the rest of the formula) Each small square is a propositional clause, e.g. (a + b ’ )

155. Traditional UC Extraction: Stage 2: Extract a Clause-Level UC An interesting constraint The remainder (the rest of the formula) Colored squares belong to the clause-level UC

156. Traditional UC Extraction: Stage 3: Map the Clause-Level UC Back to the Interesting Constraints An interesting constraint The remainder (the rest of the formula) The UC contains three interesting constraints

157. High-Level Unsatisfiable Core Extraction Real-world applications require reducing the number of interesting constraints in the core rather than clauses Latches for abstraction refinement Assumptions for compositional FEV Most of the algorithms for UC extraction are clause-level High-level UC: extracting a UC in terms of interesting constraints only Liffiton&Sakallah, 2008; Nadel, 2010; Ryvchin&Strichman, 2011

158. Small/Minimal Clause-Level UC  Small/Minimal High-Level UC A small clause-level UC, but the high-level UC is the largest possible: A large clause-level UC, but the high-level UC is empty:

159. High-Level Unsatisfiable Core Extraction: Main Results Minimal UC extraction: high-level algorithms solve Intel families that are out of reach for clause-level algorithms Non-minimal UC extraction: high-level algorithms are preferable 2-3x boost on difficult benchmarks

160. 160 Thanks!