Carnegie Mellon University

SAT-Based Decision SAT-Based Decision Procedures for Subsets of Procedures for Subsets of

First-Order Logic First-Order Logic

SAT-Based Decision SAT-Based Decision Procedures for Subsets of Procedures for Subsets of

First-Order Logic First-Order Logic

http://www.cs.cmu.edu/~bryant

Randal E. Bryant

Part I:Part I:Equality with Uninterpreted FunctionsEquality with Uninterpreted Functions

Part I:Part I:Equality with Uninterpreted FunctionsEquality with Uninterpreted Functions

– 2 –

Overall OutlineOverall Outline

BackgroundBackground SAT-based Decision Procedures

Part I: Equality with Uninterpreted FunctionsPart I: Equality with Uninterpreted Functions Translating to propositional formula Exploiting positive equality and sparse transitivity

Part II: Separation LogicPart II: Separation Logic Restricted form of addition Translating to propositional formula Hybrid encoding techniques

– 3 –

Decision Procedures in Formal VerificationDecision Procedures in Formal Verification

RTL/ Sourc

e Code

+Specif

i-cation

Abstraction Verification OK

Error

Formal

Model+

Specifi-

cation

Decision Procedure for Decidable Fragment of First-Order Logic

Decision Procedure for Decidable Fragment of First-Order Logic

Applications: Out-of-order, Pipelined Microprocessors; Cache Coherence Protocols; Device Drivers; Compiler

Validation; …

– 4 –

SAT-based Decision ProceduresSAT-based Decision Procedures

Input Formula

Boolean Formula

satisfiable unsatisfiable

Satisfiability-preserving Boolean

Encoder

SAT Solver

EAGER ENCODING

Input Formula

Boolean Formula

satisfiable

unsatisfiable

Approximate Boolean Encoder

SAT Solver satisfying assignment

satisfiable

First-order Conjunctions SAT Checker

unsatisfiableadditional clause

LAZY ENCODING

– 5 –

Lazy Encoding CharacteristicsLazy Encoding Characteristics

+ Can be extended to handle wide variety of theories+ Clean & modular design– Does not scale well

Number of calls to conjunction checker typically exponential in formula size

Each call independent: nothing learned in one call can be exploited by another

First-order Conjunctions SAT Checker

UninterpretedFunctions

LinearArithmetic

Bit Vectors

Theory N

•••

TheoryCombiner

– 6 –

Eager Encoding CharacteristicsEager Encoding Characteristics– Must encode all information about

domain properties into Boolean formula

– Some properties can give exponential blowup

+ Lets SAT solver do all of the work

Good Approach for Some DomainsGood Approach for Some Domains Modern SAT solvers have remarkable

capacityGood at extracting relevant portions out

of very large formulasLearns about formula properties as

search proceeds

Focus of this talkFocus of this talk

Input Formula

Boolean Formula

satisfiable unsatisfiable

Satisfiability-preserving Boolean

Encoder

SAT Solver

– 7 –

x0x1

x2

xn-1

Data and Function AbstractionData and Function Abstraction

ALU

x

f

Bit-vectors to (unbounded) Integers

Functional units to Uninterpreted Functions a = x b = y f(a,b) = f(x,y)

Common Operations

1

0

x

y

p

ITE(p, x, y)

If-then-else

x

y x = y=

Test for equality

– 8 –

Abstract Modeling of MicroprocessorAbstract Modeling of Microprocessor

For any Block that Transforms or Evaluates Data:For any Block that Transforms or Evaluates Data: Replace with generic, unspecified function Also view instruction memory as function

Reg.File

IF/ID

InstrMem

+4

PCID/EX

ALU

EX/WB

=

=

Rd

Ra

Rb

Imm

Op

Adat

Control Control

F1

F 2

F3

– 9 –

EUF: Equality with Uninterp. FunctsEUF: Equality with Uninterp. Functs Decidable fragment of first order logic

Formulas (Formulas (F F )) Boolean ExpressionsBoolean ExpressionsF, F1 F2, F1 F2 Boolean connectives

T1 = T2 Equation

P (T1, …, Tk) Predicate application

Terms (Terms (T T )) Integer ExpressionsInteger ExpressionsITE(F, T1, T2) If-then-else

Fun (T1, …, Tk) Function application

Functions (Functions (FunFun)) Integer Integer Integer Integerf Uninterpreted function symbolRead, Write Memory operations

Predicates (Predicates (PP)) Integer Integer Boolean Booleanp Uninterpreted predicate symbol

– 10 –

EUF Decision ProblemEUF Decision ProblemCircuit Representation of FormulaCircuit Representation of Formula

Truth ValuesDashed LinesModel ControlLogical connectivesEquations

Integer ValuesSolid linesModel DataUninterpreted functions If-Then-Else operation

TaskTask Determine whether formula F is universally valid

True for all interpretations of variables and function symbolsOften expressed as (un)satisfiability problem

» Prove that formula F is not satisfiable

=

f

T

F

T

F

fT

F

=

e1

e0x0

d0

– 11 –

Finite Model Property for EUFFinite Model Property for EUF

ObservationObservation Any formula has limited number of distinct expressions Only property that matters is whether or not different terms

are equal

=

f

T

F

T

F

fT

F

=

e1

e0x0

d0

x0 d0 f (x0) f (d0)

– 12 –

Boolean Encoding of Integer ValuesBoolean Encoding of Integer Values

For Each ExpressionFor Each Expression Either equal to or distinct from each preceding expression

Boolean EncodingBoolean Encoding Use Boolean values to encode integers over small range EUF formula can be translated into propositional logic

Logic circuit with multiplexors, comparators, logic gates Tautology iff original formula valid

ExpressionExpression Possible Possible ValuesValues

Bit Bit EncodingEncoding

x0 {0}{0} 00 00

d0 {0,1}{0,1} 00 bb1010

f (x0) {0,1,2}{0,1,2} bb2121 bb2020

f (d0) {0,1,2,3}{0,1,2,3} bb3131 bb3030

– 13 –

Some History of EUF Decision ProceduresSome History of EUF Decision Procedures

Ackermann, 1954Quantifier-free decision problem can be decided based on finite

instantiations

Burch & Dill, CAV ‘94Automatic decision procedure

» Davis-Putnam enumeration

» Congruence closure to enforce functional consistency

Boolean approachesGoel, et al, CAV ‘98

» Attempted with BDDs, but didn’t get good resultsBryant, German, Velev, CAV ‘99

» Could verify microprocessor using BDDsVelev & Bryant, DAC 2001

» Demonstrated power of modern SAT procedures

– 14 –

Exploiting Positive EqualityExploiting Positive Equality

Bryant, German, Velev CAV ‘99 First successful use of Boolean methods for EUF

Positive EqualityPositive Equality Equations that appear in unnegated form

ExploitingExploiting Can greatly reduce number of cases required to show

validityOnly need to consider maximally diverse interpretations

Reduce number of Boolean variables in bit-level encoding

– 15 –

Diverse Interpretations: IllustrationDiverse Interpretations: Illustration

TaskTask Verify someone’s obscure code for 4X4 array transpose

void trans(int a[4][4]){ int t; for (t = 4; t < 15; t++) if (~t&2|| t&8 && ~t&1) { int r = t&0x3; int c = t>>2; int val = a[r][c]; a[r][c] = a[c][r]; a[c][r] = val; }}

ObservationObservation Array elements altered only by copying one to another Just need to make sure right set of copies performed

Only operations on array elements

– 16 –

Verifying Array CodeVerifying Array Code

Test for Test for trans4trans4

trans4

Single Test AdequateSingle Test Adequate Unique value for each possible source element

“Maximally Diverse”

If dest[r][c] = src[c][r], then must have copied proper value

src dest

0 1 2 3

4 5 6 7

8 9 10 11

12 13 14 15

0 4 8 12

1 5 9 13

2 6 10 14

3 7 11 15

– 17 –

Characteristics of Array VerificationCharacteristics of Array Verification

Correctness ConditionCorrectness Condition

src[0][0]src[0][0] = = dest[0][0] dest[0][0] src[0][1] src[0][1] = = dest[1][0] dest[1][0]

src[0][2]src[0][2] = = dest[2][0] dest[2][0] … …

… …

src[3][2]src[3][2] = = dest[2][3] dest[2][3] src[3][3] src[3][3] = = dest[3][3]dest[3][3]

PropertiesProperties All equations are in positive form Worst case test is one that tends to make things unequal

I.e., maximally diverse interpretation

All maximally diverse interpretations isomorphicOnly need to try one to prove all handled correctly

– 18 –

Equations in Processor VerificationEquations in Processor Verification

Data TypesData Types EquationsEquations Register Ids Control stalling & forwarding Instruction Address Only top-level verification condition Program Data Only top-level verification condition

Reg.File

IF/ID

InstrMem

+4

PCID/EX

ALU

EX/WB

=

=

Rd

Ra

Rb

Imm

Op

Adat

Control Control

– 19 –

Exploiting Equation StructureExploiting Equation Structure

Positive EquationsPositive Equations In top-level verification condition Can use maximally diverse interpretation

Negative EquationsNegative Equations PIpeline control logic

Between register IDsOperation depends on whether or not two IDs are equal

Must use general encodingEncode with Boolean variablesAll possibility of IDs that match and/or don’t match

– 20 –

Application of Positive EqualityApplication of Positive Equality

ObservationObservation All equations are positive in this formula Can consider single, diverse interpretation for terms

=

f

T

F

T

F

fT

F

=

e1

e0x0

d0

x0 d0 f (x0) f (d0)

5 6 7 8

01

0 1

5

65 6

7

7 8

5 67 8

5 67 6

1

– 21 –

f

fvf1

vf2

Function Elimination: Ackermann’s MethodFunction Elimination: Ackermann’s MethodReplace All Function Applications by Integer VariablesReplace All Function Applications by Integer Variables

Introduce new domain variable Enforce functional consistency by global constraints

Unclear how to restrict evaluation to diverse interpretations

x1

x2

F= =

– 22 –

f

f

fx1

x2

x3

vf1

vf2

T

F

=

==

T

F

vf3

T

F

Function Elimination: ITE MethodFunction Elimination: ITE Method

General TechniqueGeneral Technique Introduce new domain variable Nested ITE structure maintains functional consistency

– 23 –

f

f

fx1

x2

x3

5

6T

F

=

==

T

F

7

T

F

Generating Diverse EncodingGenerating Diverse Encoding

Replacing ApplicationReplacing Application Use fixed values rather than variables Application results equal iff arguments equal

– 24 –

Benefits of Positive EqualityBenefits of Positive EqualityMicroprocessor BenchmarksMicroprocessor Benchmarks

1xDLX: Single issue, RISC processor 2xDLX-EX-BP: Dual issue processor with exception handling & branch

prediction 9VLIW-BP: 9-way VLIW processor with branch prediction

MeasurementsMeasurements Using BerkMin SAT solver

Benchmark Using Pos. Eq. No Pos. Eq

1xDLXbuggy 0.02 2

good 0.07 229

2xDLX-EX-BPbuggy 4 15

good 15 > 24hrs

9VLIW-BPbuggy 10 > 24hrs

good 224 > 24hrs

– 25 –

Benefits of Positive EqualityBenefits of Positive EqualityMicroprocessor BenchmarksMicroprocessor Benchmarks

1xDLX: Single issue, RISC processor 2xDLX-EX-BP: Dual issue processor with exception handling & branch

prediction 9VLIW-BP: 9-way VLIW processor with branch prediction

MeasurementsMeasurements Using BerkMin SAT solver

Benchmark Using Pos. Eq. No Pos. Eq

1xDLXgood 0.02 2

buggy 0.07 229

2xDLX-EX-BPgood 4 15

buggy 15 > 24hrs

9VLIW-BPgood 10 > 24hrs

buggy 224 > 24hrs

Velev & Bryant, JSC ‘02

– 26 –

Revisiting Encoding TechniquesRevisiting Encoding Techniques

Small Domain (SD)Small Domain (SD)

Use bit-level encodings of bounded integers Implicitly encode properties of equality logic

Per-Constraint Encoding (EIJ)Per-Constraint Encoding (EIJ)

Introduce explicit Boolean variable for each equation Additional transitivity constraints to express properties of

equality logic

x = y y = z z x

x1x0 = y1y0 y1y0 = z1z0 z1z0 x1x0

exy eyz exz

Satisfiable?

eyz ezx exy exy eyz exz exy exz eyz

Transitivity Constraints

– 27 –

Per-Constraint EncodingPer-Constraint Encoding

Introduced by Goel et al., CAV ‘98Exploiting sparse structure by Bryant & Velev, CAV 2000

ProcedureProcedure Initial formula F

Want to prove validProve that F is not satisfiable

Replace each equation x = y by Boolean variable exy

Gives formula Fsat

Generate formula expressing transitivity constraintsGives formula Ftrans

Use SAT solver to show that Fsat Ftrans not satisfiable

MotivationMotivation Provides SAT solver with more direct representation of

underlying problem

– 28 –

Graph Interpretation of TransitivityGraph Interpretation of Transitivity

Transitivity ViolationTransitivity Violation Cycle in graph Exactly one edge has ei,j = false

== ==

==

==

====

==

– 29 –

Exploiting ChordsExploiting Chords

ChordChord Edge connecting two non-

adjacent vertices in cycle

PropertyProperty Sufficient to enforce

transitivity constraints for all chord-free cycles

If transitivity holds for all chord-free cycles, then holds for arbitrary cycles

– 30 –

Enumerating Chord-Free CyclesEnumerating Chord-Free Cycles

StrategyStrategy Enumerate chord-free cycles in graph Each cycle of length k yields k transitivity constraints

• • •

1 2 k• • •

ProblemProblem Potentially exponential number of chord-free cycles

2k+k chord-free cycles

– 31 –

Adding ChordsAdding Chords

StrategyStrategy Add edges to graph to reduce number of chord-free cycles

• • •

1 2 k• • •2k+k chord-free cycles

2k+1 chord-free cycles

Trade-OffTrade-Off Reduces formula size Increases number of relational variables

– 32 –

Chordal GraphChordal Graph

DefinitionDefinition Every cycle of length > 3 has a

chord

GoalGoal Add minimum number of edges

to make graph chordal

Relation to Sparse Gaussian Relation to Sparse Gaussian EliminationElimination

Choose pivot ordering that minimizes fill-in

NP-hard Simple heuristics effective

– 33 –

1xDLX-C Equation Structure1xDLX-C Equation Structure

VerticesVertices For each vi

13 different register identifiers

EdgesEdges For each equation Control stalling and

forwarding logic 27 relational variables

Out of 78 possible

– 34 –

Adding Chordal Edges to 1xDLX-CAdding Chordal Edges to 1xDLX-C

OriginalOriginal 27 relational variables 286 cycles 858 clauses

AugmentedAugmented 33 relational

variables 40 cycles 120 clauses

– 35 –

2DLX-CCt Equation Structure2DLX-CCt Equation Structure

EquationsEquations Between 25

different register identifiers

143 relational variables

Out of 300 possible

– 36 –

Adding Chordal Edges to 2xDLX-CCtAdding Chordal Edges to 2xDLX-CCt

OriginalOriginal 143 relational

variables 2,136 cycles 8,364 clauses

AugmentedAugmented 193 relational

variables 858 cycles 2,574 clauses

– 37 –

Choosing Encoding MethodChoosing Encoding Method

ComparisonComparison Formula length n with m integer variables & function

applications Worst-case complexity

Per-Constraint Encoding Works Well in PracticePer-Constraint Encoding Works Well in Practice Generates slightly larger formulas than small domain Better performance by SAT solver

Small Domain Per-Constraint

Boolean Variables

O(m log m) O(m2)

Formula Size O(n + m2 log m) O(n + m3)

– 38 –

Encoding ComparisonEncoding ComparisonBenchmarksBenchmarks

Superscalar, out-of-order datapath 2–6 instructions issued in parallel

MeasurementsMeasurements Using BerkMin SAT solver

Issue Width

Per-Constraint Small Domain

Vars Clauses Time Vars Clauses Time

2 139 8,213 1.6 81 1,294 1.7

3 308 33,270 15 127 3,780 19

4 553 96,480 65 194 8,362 99

5 857 240,892 154 249 15,647 255

6 1,243 528,962 1,957 304 26,738 3,206

Velev & Bryant, JSC ‘02