Craig Interpolation for Integer Arithmetic: Results ... · Outline Craig Interpolation for...

Craig Interpolation for Integer Arithmetic:Results, Implementation, Experiences

Philipp Rummer

Uppsala University

IWIL WorkshopMarch 10th, 2012

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Outline

Craig Interpolation for Presburger Arithmetic

� Motivation

� Craig’s theorem

� Results and methods for integers

Implementation, Experiences

� Implementation in the theorem prover Princess

� Experiences with Scala for solvers

� Some experimental data

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Motivation: inference of invariants

Generic verification problem (“safety”)

{ pre } while (*) Body { post }

Standard approach: loop rule using invariant

pre⇒ φ { φ } Body { φ } φ⇒ post

{ pre } while (*) Body { post }

How to compute φ automatically?

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From intermediate assertions to invariants

{pre} Body; Body {post} ?

Bounded model checking problem !

Compute intermediate assertion ψ1

{pre} Body {ψ1} {ψ1} Body {post}

pre is invariant !

[ψ1 ⇒ pre] [otherwise]

[McMillan, 2003]4 / 84

pre is invariant !

[ψ1 ⇒ pre]

[otherwise]

[McMillan, 2003]4 / 84

pre is invariant !

[McMillan, 2003]4 / 84

{pre ∨ ψ1} Body; Body {post} ?

{pre ∨ ψ1} Body {ψ2} {ψ2} Body {post}

pre is invariant !

[McMillan, 2003]4 / 84

pre ∨ ψ1 is invariant !

[ψ2 ⇒ pre ∨ ψ1] [otherwise]

[McMillan, 2003]4 / 84

pre ∨ ψ1 is invariant !

[ψ2 ⇒ pre ∨ ψ1] . . .

[McMillan, 2003]4 / 84

How to compute intermediate assertions?

{ pre }

VC generation

pre (s0)Body; → Body (s0, s1)Body → Body (s1, s2)

{ post } → post (s2)

Theorem (Craig, 1957)

Suppose A→ C is a valid FOL implication.Then there is a formula I (an interpolant) such that

� A→ I and I → C are valid,

� every non-logical symbol of I occurs in both A and C .

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{ pre }

VC generation

pre (s0)Body; → Body (s0, s1)Body → Body (s1, s2)

{ post } → post (s2)

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{ pre }

VC generation

pre (s0)Body; → Body (s0, s1)

I (s1)

A(s0, s1)

C(s1, s2)

Body → Body (s1, s2){ post } → post (s2)

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Illustration

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Interpolation problem: A→ I → C

Illustration

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Example

Program with assertion:

if (a == 2*x && a >= 0) {

b = a / 2;

c = 3*b + 1;

assert (c > a);

As a verification condition:

a = 2*x & a >= 0

2*b <= a & a <= 2*b + 1

c = 3*b + 1

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Example

Program with assertion:

if (a == 2*x && a >= 0) {

b = a / 2;

c = 3*b + 1;

assert (c > a);

As a verification condition:

a = 2*x & a >= 0

2*b <= a & a <= 2*b + 1

-> // Interpolant: 3*b >= a

c = 3*b + 1

-> // Interpolant: c >= a + 1

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Other applications of interpolation

� Blocking lemmas for test-case generation

� Refinement of abstractions in CEGAR

� Computation of summaries

� Synthesis

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Interpolation + theories

Interpolation procedures need to support the program logic:

i n t a [ ] , i ;max = a [ 0 ] ;f o r ( i = 1 ; i < n ; ++i )

i f ( a [ i ] > max)max = a [ i ] ;

a s s e r t (max >= a [ i / 2 ] ) ;

E.g., combined use of linear integer arithmetic and arrays

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Relevant questions, given a logic L

� Is L closed under interpolation?

� Practical interpolation procedures for L

Definition

Logic L is closed under interpolation iffor all A,B ∈ F such that A⇒ B, there is an interpolantexpressible in L.

� In particular:Is quantifier-free fragment of L closed under interpolation?

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Interpolation for integers

Presburger Arithmetic (QPA)

t ∶∶= α ∣ c ∣ x ∣ αt +⋯ + αt

φ ∶∶= φ ∧ φ ∣ φ ∨ φ ∣ ¬φ ∣ φ→ φ ∣ ∀x .φ ∣ ∃x .φ

∣ t ≐ 0 ∣ t ≤ 0 ∣ α ∣ t

t . . . terms

φ . . . formulae

x . . . variables

c . . . constant symbols

α . . . integer literals (Z)

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Interpolation for integers

Presburger Arithmetic (QPA)

t ∶∶= α ∣ c ∣ x ∣ αt +⋯ + αt

φ ∶∶= φ ∧ φ ∣ φ ∨ φ ∣ ¬φ ∣ φ→ φ ∣ ∀x .φ ∣ ∃x .φ

∣ t ≐ 0 ∣ t ≤ 0 ∣ α ∣ t

Mainly considered here: the quantifier-free fragment (PA)

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Interpolation by quantifier elimination (QE)

Theorem (QE for Presburger Arithmetic)

For every formula φ in full QPA, there is an equivalentquantifier-free formula ψ that can effectively be computed.

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Interpolation by quantifier elimination (2)

If A→ C is a valid implication, then

� ∃local-syms(A)(A) is the strongest interpolant,

� ∀local-syms(C)(C) is the weakest interpolant.

local-syms(A): symbols occurring in A, but not in Clocal-syms(C): . . .

Corollary

Both PA and QPA are closed under interpolation.

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Interpolation vs. QE

However . . .

� QE has high computational complexity

� strongest and weakest interpolants are often notneeded/desirable⇒ Larger interpolants, containing irrelevant information

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Proof-based interpolation techniques

Theorem prover

Implication A→ C

Proof of A→ C

Proof lifting

Interpolating proof of A→ C

Craig interpolant A→ I → C

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Abstraction with interpolants

Interpolant extractedfrom proof

⇒Abstraction from

unnecessary details

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Abstraction with interpolants

⋯ Interpolant extractedfrom proof

⇒Abstraction from

unnecessary details

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Towards practical integer interpolation procedures

� Difference logic[McMillan, 2006]

� Integer equalities + divisibility constraints[Jain, Clarke, Grumberg, 2008]

� Unit-two-variable-per-inequality[Cimatti, Griggio, Sebastiani, 2009]

� Simplex-based, full PA[Lynch, Tang, 2008]⇒ Leaves local symbols/quantifiers in interpolants

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Towards practical interpolation procedures (2)

Proof-based methods for full PA:

� Sequent calculus-based[Brillout, Kroening, Rummer, Wahl, 2010]

� Simplex-based, special branch-and-cut rule[Kroening, Leroux, Rummer, 2010]

� Simplex-based, targeting SMT[Griggio, Le, Sebastiani, 2011]

� From Z3 proofs[McMillan, 2011]

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What makes interpolation over integers difficult?

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Reverse interpolants

Definition

Suppose A ∧B is unsatisfiable.A reverse interpolant is a formula I such that

� A→ I and B → ¬I are valid,

� every non-logical symbol of I occurs in both A and B.

I is reverse interpolant for A ∧B⇐⇒

I is interpolant for A→ ¬B

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Consider rational case:n

⋀i=1

ti ≤ 0

´¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¶A

⋀j=1

sj ≤ 0

´¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¶B

Lemma (Witnesses)

A ∧B is unsat over Q iffthere are non-negative {αi}ni=1,{βj}mj=1 such that:

∑i=1αi ti +

∑j=1βjsj ∈Q>0

Then:n

∑i=1αi ti ≤ 0 is a reverse interpolant

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⋀i=1

ti ≤ 0

´¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¶A

⋀j=1

sj ≤ 0

´¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¶B

Lemma (Witnesses)

∑i=1αi ti +

∑j=1βjsj ∈Q>0

Then:n

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⋀i=1

ti ≤ 0

´¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¶A

⋀j=1

sj ≤ 0

´¹¹¹¹¹¹¹¹¹¹¹¹¸¹¹¹¹¹¹¹¹¹¹¹¹¶B

Lemma (Witnesses)

∑i=1αi ti +

∑j=1βjsj ∈Q>0

Then:n

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What makes interpolation over integers difficult? (2)

Why does this not work for integers?

Over Z, additional rules are needed, such as:

� Branch-and-bound(unproblematic, but incomplete)

� Cutting planes, Gomory cuts

� Cuts-from-proofs

� Omega rule

⇒ Interpolation more intricate

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Why does this not work for integers?

Over Z, additional rules are needed, such as:

� Branch-and-bound(unproblematic, but incomplete)

� Cutting planes, Gomory cuts

� Cuts-from-proofs

� Omega rule

⇒ Interpolation more intricate

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Theorem

There is a family {An ∧Bn}n of PA formulae such that

� An ∧Bn is unsatisfiable,

� An ∧Bn has a cutting plane proof of size independent of n,

� all reverse interpolants have size at least linear in n.

(for the definition of PA shown earlier)

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Example:

An = − n < y + 2nx ∧ y + 2nx ≤ 0

Bn = 0 < y + 2nz ∧ y + 2nz ≤ n

All reverse interpolants for An ∧Bn are equivalent to:

In = (2n ∣ y) ∨ (2n ∣ y + 1) ∨ (2n ∣ y + 2) ∨⋯ ∨ (2n ∣ y + n − 1)

Problematic: mixed cuts

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Example:

An = − n < y + 2nx ∧ y + 2nx ≤ 0

Bn = 0 < y + 2nz ∧ y + 2nz ≤ n

All reverse interpolants for An ∧Bn are equivalent to:

In = (2n ∣ y) ∨ (2n ∣ y + 1) ∨ (2n ∣ y + 2) ∨⋯ ∨ (2n ∣ y + n − 1)

Problematic: mixed cuts

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Three main approaches to handle mixed cuts

� Fully expanded interpolants

� Restricted/taylor-made cut rule

� Extended interpolant language

Next:Comparison + unifying description

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Interpolation outline

Theorem prover

Implication A→ C

Proof of A→ C

Proof lifting

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Theorem prover

Implication A→ C

Proof of A→ C

Proof lifting

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Main non-interpolating proof rules

Closure rule (α > 0)

∗Γ, α ≤ 0 ⊢ ∆

close-ineq′

Linear combination of inequalities (α > 0, β > 0)

Γ, . . . , αs + βt ≤ 0 ⊢ ∆

Γ, s ≤ 0, t ≤ 0 ⊢ ∆ fm-elim′

Strengthening inequalities (subsumes rounding + cuts)

Γ, t ≐ 0 ⊢ ∆ Γ, t + 1 ≤ 0 ⊢ ∆

Γ, t ≤ 0 ⊢ ∆ strengthen′

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Example of non-interpolating proof

∗. . . , 3 ≤ 0 ⊢ ineq-close′

. . . , 3x ≤ 0, −2x + 1 ≤ 0 ⊢ fm-elim′⋯

. . . , 3x − 2 ≤ 0, −2x + 1 ≤ 0 ⊢ strengthen′ × 2

a + x ≤ 0, −a + 2x − 2 ≤ 0, −2x + 1 ≤ 0 ⊢ fm-elim′

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Theorem prover

PA implication A→ C

Proof of A→ C

Proof lifting

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Basic idea of proof lifting

annotation offormulae with labels

Õ××××

∗....Γ3 ⊢ ∆3

Γ2 ⊢ ∆2

Γ1 ⊢ ∆1....A ⊢ C

××××Öpropagation of

interpolants

Main idea: annotations track inequalities from A

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Õ××××

∗....Γ3 ⊢ ∆3

Γ2 ⊢ ∆2

Γ1 ⊢ ∆1....A ⊢ C

interpolants

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Õ××××

∗....Γ3 ⊢ ∆3

Γ2 ⊢ ∆2

Γ1 ⊢ ∆1....A∗ ⊢ C∗

interpolants

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Õ××××

∗....Γ3 ⊢ ∆3

Γ2 ⊢ ∆2

Γ∗1 ⊢ ∆∗1....

A∗ ⊢ C∗

interpolants

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Õ××××

∗....Γ3 ⊢ ∆3

Γ∗2 ⊢ ∆∗2

Γ∗1 ⊢ ∆∗1....

A∗ ⊢ C∗

interpolants

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Õ××××

∗....Γ∗3 ⊢ ∆∗

Γ∗2 ⊢ ∆∗2

Γ∗1 ⊢ ∆∗1....

A∗ ⊢ C∗

interpolants

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Õ××××

∗....Γ∗3 ⊢ ∆∗

Γ∗2 ⊢ ∆∗2

Γ∗1 ⊢ ∆∗1....

A∗ ⊢ C∗

interpolants

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Õ××××

∗....Γ∗3 ⊢ ∆∗

3 ▸ I3

Γ∗2 ⊢ ∆∗2

Γ∗1 ⊢ ∆∗1....

A∗ ⊢ C∗

interpolants

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Õ××××

∗....Γ∗3 ⊢ ∆∗

3 ▸ I3

Γ∗2 ⊢ ∆∗2 ▸ I2

Γ∗1 ⊢ ∆∗1....

A∗ ⊢ C∗

interpolants

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Õ××××

∗....Γ∗3 ⊢ ∆∗

3 ▸ I3

Γ∗2 ⊢ ∆∗2 ▸ I2

Γ∗1 ⊢ ∆∗1 ▸ I1....

A∗ ⊢ C∗

interpolants

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Õ××××

∗....Γ∗3 ⊢ ∆∗

3 ▸ I3

Γ∗2 ⊢ ∆∗2 ▸ I2

Γ∗1 ⊢ ∆∗1 ▸ I1....

A∗ ⊢ C∗ ▸ I

interpolants

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Labelled formulae

Labelled formula Intuition

φ [φA] “φA is A-contribution to φ”φA is the partial interpolant of φ

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Interpolating rules

Initialisation rule: t ≤ 0 comes from A

Γ, t ≤ 0 [t ≤ 0] ⊢ ∆ ▸ I

Γ, t ≤ 0 ⊢ ∆ ▸ Iipi-left-l

Initialisation rule: t ≤ 0 comes from C

Γ, t ≤ 0 [0 ≤ 0] ⊢ ∆ ▸ I

Γ, t ≤ 0 ⊢ ∆ ▸ Iipi-left-r

� Similarly for equations, etc.

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Interpolating rules

Closure rule (α > 0)

∗Γ, α ≤ 0 [tA ≤ 0] ⊢ ∆ ▸ tA ≤ 0

close-ineq

Linear combination of inequalities (α > 0, β > 0)

Γ, . . . , αs + βt ≤ 0 [αsA + βtA ≤ 0] ⊢ ∆ ▸ I

Γ, s ≤ 0 [sA ≤ 0], t ≤ 0 [tA ≤ 0] ⊢ ∆ ▸ Ifm-elim

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How to interpolate strengthen′?

Γ, t ≐ 0 ⊢ ∆ Γ, t + 1 ≤ 0 ⊢ ∆

Γ, t ≤ 0 ⊢ ∆ strengthen′

Three sound & complete ways . . .

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1. Method: only do pure strengthening

Pure strengthen

Γ, t ≐ 0 [t ≐ 0] ⊢ ∆ ▸ IΓ, t + 1 ≤ 0 [t + 1 ≤ 0] ⊢ ∆ ▸ J

Γ, t ≤ 0 [t ≤ 0] ⊢ ∆ ▸ I ∨ Jstrengthen-l

Γ, t ≐ 0 [0 ≐ 0] ⊢ ∆ ▸ IΓ, t + 1 ≤ 0 [0 ≤ 0] ⊢ ∆ ▸ J

Γ, t ≤ 0 [0 ≤ 0] ⊢ ∆ ▸ I ∧ Jstrengthen-r

� Resembles Omega test

� Can lead to large proofs, but interpolants of linear size

� Integration with Simplex in [LPAR, 2010]⇒ Special branch-and-cut rule

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1. Method: only do pure strengthening

Pure strengthen

Γ, t ≐ 0 [t ≐ 0] ⊢ ∆ ▸ IΓ, t + 1 ≤ 0 [t + 1 ≤ 0] ⊢ ∆ ▸ J

Γ, t ≤ 0 [t ≤ 0] ⊢ ∆ ▸ I ∨ Jstrengthen-l

Γ, t ≐ 0 [0 ≐ 0] ⊢ ∆ ▸ IΓ, t + 1 ≤ 0 [0 ≤ 0] ⊢ ∆ ▸ J

Γ, t ≤ 0 [0 ≤ 0] ⊢ ∆ ▸ I ∧ Jstrengthen-r

� Resembles Omega test

� Can lead to large proofs, but interpolants of linear size

� Integration with Simplex in [LPAR, 2010]⇒ Special branch-and-cut rule

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Interpolating proof for previous example

∗. . . , 3 ≤ 0 [6x ≤ 0] ⊢ ▸ x ≤ 0

. . . , 3x ≤ 0 [3x ≤ 0], −2x + 1 ≤ 0 [0 ≤ 0] ⊢ ▸ x ≤ 0

. . . , 3x − 2 ≤ 0 [3x − 2 ≤ 0], −2x + 1 ≤ 0 [0 ≤ 0] ⊢ ▸ x ≤ 0

a + x ≤ 0 [a + x ≤ 0],−a + 2x − 2 ≤ 0 [−a + 2x − 2 ≤ 0],

−2x + 1 ≤ 0 [0 ≤ 0]⊢ ▸ x ≤ 0

Original proof

∗. . . , 3 ≤ 0 ⊢ ineq-close′

. . . , 3x ≤ 0, −2x + 1 ≤ 0 ⊢ fm-elim′⋯

. . . , 3x − 2 ≤ 0, −2x + 1 ≤ 0 ⊢ strengthen′ × 2

a + x ≤ 0, −a + 2x − 2 ≤ 0, −2x + 1 ≤ 0 ⊢ fm-elim′

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2. Method: allow mixed strengthening

General, mixed strengthen (“mixed cuts”)

Γ, t ≐ 0 [tA ≐ 0] ⊢ ∆ ▸ E

Γ, t + 1 ≤ 0 [tA ≤ 0] ⊢ ∆ ▸ I 0

Γ, t + 1 ≤ 0 [tA + 1 ≤ 0] ⊢ ∆ ▸ I 1

Γ, t ≤ 0 [tA ≤ 0] ⊢ ∆ ▸ I 1 ∨ (E ∧ I 0)strengthen

� Covers Omega test, Gomory cuts, etc.

� Interpolants can beexponentially larger than (non-interpolating) proofs

� Sometimes observed in practice:Proof can be constructed, but proof lifting times out

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2. Method: allow mixed strengthening

General, mixed strengthen (“mixed cuts”)

Γ, t ≐ 0 [tA ≐ 0] ⊢ ∆ ▸ E

Γ, t + 1 ≤ 0 [tA ≤ 0] ⊢ ∆ ▸ I 0

Γ, t + 1 ≤ 0 [tA + 1 ≤ 0] ⊢ ∆ ▸ I 1

� Covers Omega test, Gomory cuts, etc.

� Interpolants can beexponentially larger than (non-interpolating) proofs

� Sometimes observed in practice:Proof can be constructed, but proof lifting times out

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3. Method: bounded quantification

strengthen with bounded quantification

� Proofs + interpolants only grow linearly

� Interpolants contain bounded quantifiers

� Specialised versions for rounding possible [IJCAR, 2010]

� Related observation in [Griggio, Le, Sebastiani, 2011]:Mixed cuts can be interpolated concisely using integer division

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Γ, t ≐ 0 [tA ≐ 0] ⊢ ∆ ▸ E

Γ, t + 1 ≤ 0 [tA ≤ 0] ⊢ ∆ ▸ I 0

Γ, t + 1 ≤ 0 [tA + 1 ≤ 0] ⊢ ∆ ▸ I 1

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Γ, t ≐ 0 [tA ≐ 0] ⊢ ∆ ▸ E

Γ, t + 1 ≤ 0 [tA ≤ 0] ⊢ ∆ ▸ I 0

Γ, t + 1 ≤ 0 [tA + 1 ≤ 0] ⊢ ∆ ▸ I 1

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Γ, t ≐ 0 [tA ≐ 0] ⊢ ∆ ▸ E

Γ, t + 1 ≤ 0 [tA+p ≤ 0] ⊢ ∆ ▸ I (p)Γ, t ≤ 0 [tA ≤ 0] ⊢ ∆ ▸ I (1) ∨ (E ∧ I (0))

strengthen

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Γ, t ≐ 0 [tA ≐ 0] ⊢ ∆ ▸ E

Γ, t + 1 ≤ 0 [tA+p ≤ 0] ⊢ ∆ ▸ I (p)

Γ, t ≤ 0 [tA ≤ 0] ⊢ ∆ ▸ ∃0 ≤ p ≤ 1.I (p) ∧ (p ≐ 1 ∨ E)

strengthen-bq

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Γ, t ≐ 0 [tA ≐ 0] ⊢ ∆ ▸ E

Γ, t + 1 ≤ 0 [tA+p ≤ 0] ⊢ ∆ ▸ I (p)

Γ, t ≤ 0 [tA ≤ 0] ⊢ ∆ ▸ ∃0 ≤ p ≤ 1.I (p) ∧ (p ≐ 1 ∨ E)

strengthen-bq

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Combining method 2 + 3

Observation, in practice:QE methods often eliminate bounded quantifiers without blowup

Theorem prover

Implication A→ C

Proof of A→ C

Proof lifting(method 2)

Quantifier-freeinterpolant I

Interpolant I ′ withbounded quantifiers

Quantifierelimination

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Theorem prover

Implication A→ C

Proof of A→ C

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Theorem prover

Implication A→ C

Proof of A→ C

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Ongoing work

Elimination of bounded quantifiers . . .

� Often leads to very concise interpolants

� Sometimes causes blowup(e.g., when encoding bitvector problems)

Ongoing: better integration of methods 2 + 3

� Detect in which cases bounded quantifiers are cheap toeliminate

Also ongoing:

� Experimental comparison of methods 1+2+3,in a model checker

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Implementations

Method 1: OpenSMT [LPAR, 2010]

� Simplex-based

� Branch-and-cut rule, avoiding mixed cuts

Method 2 + 3: Princess [IJCAR, 2010]

� Omega-based

� First interpolants with bounded quantifiers,then quantifier elimination (Omega)

� http://www.philipp.ruemmer.org/princess.shtml

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About Princess

� Started in 2007, slowly moving along(name “Princess” → complicated explanation)

� Entirely implemented in Scala

� Original motivation:Explore combination of FOL + theory reasoning

� Input logic:QPA + uninterpreted predicates/functions

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Combination of different prover architectures

Experiment in Princess:

� KE-tableau/DPLL FOL

� Theory procedures Arithmetic

� E-matching Axiomatisation of theories

� Free variables + constraints Quantifiers

� Interesting completeness results

� Proof generation (used for interpolation)

� Some features that are rather unique

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(In)Completeness of the Princess calculus

Lemma (Completeness)

Complete for fragments:

� FOL

� PA

� Purely existential formulae

� Purely universal formulae

� Universal formulae with finite parametrisation(same as ME(LIA))

� Valid formulae in the full logic are not enumerable[Halpern, 1991]

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About Scala

Java + functional features� Algebraic datatypes

� Pattern matching

� Type inference

� Higher-order functions

� Monads

� Actors, concurrent datatypes

� Developed by Martin Odersky’s group, EPFL

� Compilation to Java bytecode (primarily)

� Full access to Java libraries

� Is Scala a language usable for solver implementations?

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About Scala

Java + functional features� Algebraic datatypes

� Pattern matching

� Type inference

� Higher-order functions

� Monads

� Actors, concurrent datatypes

� Developed by Martin Odersky’s group, EPFL

� Compilation to Java bytecode (primarily)

� Full access to Java libraries

� Is Scala a language usable for solver implementations?

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Some observations and thoughts on Scala . . .

Elegant APIs possible

val c = new ConstantTerm("c")

val d = new ConstantTerm("d")

val f = new IFunction("f", 1, false, false)

println(isSat(c >= 12 & c*2 < 40 & f(c-d) < 100))

Maybe more relevant for solver users than developers

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Elegant APIs possible

val c = new ConstantTerm("c")

val d = new ConstantTerm("d")

val f = new IFunction("f", 1, false, false)

println(isSat(c >= 12 & c*2 < 40 & f(c-d) < 100))

Maybe more relevant for solver users than developers

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Deployment

� Bytecode is very convenient

� However: Scala tends to generate many many classesE.g. in Princess: before compilation ≈ 350

after compilation ≈ 3000

� ProGuard (compression tool) is useful⇒ Generate one jar file, including all Scala libraries

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Performance? (disclaimer)

Princess was not developed in a very performance-oriented way:

� Mostly functional (immutable) datastructures

� No native datastructures (JNI)

� Generally:Correctness considered more important than efficiency

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Compared to other languages (Compiler shootout)

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JVM warm-upTi

Repetitions

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JVM warm-up (2)

Caused by:

� Dynamic class loading

� Just-in-time compilation + optimisation

This means:

� Restarting solver between queries has to be avoided

� Load solver as a library (jar-file), or

� Run as a daemon

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Evaluation on AUFLIA benchmarks

AUFLIA+p (193) AUFLIA-p (193)

Z3 191 191

Princess 145 137CVC3 132 128

� Unsatisfiable AUFLIA benchmarks from SMT-comp 2011

� Intel Core i5 2-core, 3.2GHz, timeout 1200s, 4Gb

� http://www.philipp.ruemmer.org/princess.shtml

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Typical PA SAT queries in a model checker (Eldarica)

Solving time Princess/ms

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Profiling Scala applications

Does not work

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Synthetic interpolation benchmarks (beginning 2011)

� Evaluation on SMT-LIB QF LIA benchmarks

� Partitionings:First k

10 ⋅ n benchmark conjuncts as A, rest as B(where n is total number of conjuncts, k ∈ {1, . . . ,9})

� Intel Xeon X5667 4-core, 3.07GHz, 12GB heap-space, Linux, timeout 900s.

http://www.philipp.ruemmer.org/princess.shtml

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Compared tools

� Princess, OpenSMT

� SMTInterpol: interpolating SMT solver from Uni Freiburg

� CSIsat: constraint-based interpolation for linear rationalarithmetic + unint. functions

� Omega: quantifier elimination procedure(strongest interpolants can be computed using QE)

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Experimental results

Multiplier Bitadder Mathsat Rings Convert16 unsat1 sat

17 unsat 100 unsat 294 unsat 38 unsat109 sat172 unkn.

Princess 8/1/41 7/0/63 44/13/396 130/0/209 38/82/334136/1623 298/76953 106/7007 233/5146 88.0/1

OpenSMT 5/1/45 7/0/63 74/15/666 9/0/81 37/0/33348.9/2357 103/23362 53.0/2020 59.9/4611 0.08/1

SMTInterpol 5/1/45 5/0/45 65/13/585 0/0/– 37/0/33324.4/48827 8.58/41077 45.7/126705 –/– 13.6/2

CSIsat 4/1/36 1/0/9 25/12/225— —

106/2640 0.56/188 70.8/12683Omega QE –/–/125 –/–/129 –/–/612 –/–/1474 –/–/296

109/15392 97.8/93181 169/101088 227/55307 15.4/2668

#unsat / #sat / #interpolants / average time (s) / average int. size

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Experimental results: interpolant sizesPrincess

Princess

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Experimental results: interpolant sizes (2)Princess

Princess

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Conclusions

Is Scala a language usable for solver implementations?

� Deployment

� Very elegant APIspossible

� Convenient

� Warm-up time of JVM

� Performance penalty stillsignificant

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Thanks for your attention!

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Craig Interpolation for Integer Arithmetic: Results ... · Outline Craig Interpolation for...

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