1 compositional methods and symbolic model checking ken mcmillan cadence berkeley labs

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Compositional Methodsand

Symbolic Model Checking

Ken McMillan

Cadence Berkeley Labs

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Compositional methods Reduce large verification problems to small ones by

– Decomposition

– Abstraction

– Specialization

– etc.

Based on symbolic model checking

System level verification

Will consider the implications of such an approach for symbolic model checking

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Example -- Cache coherence

S/F network

protocol

hostprotocol

host

protocol

host

Distributedcachecoherence

INTF

P P

M IO

to net

Nondeterministic abstract model

Atomic actions

Single address abstraction

Verified coherence, etc...

(Eiriksson 98)

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S/F networkprotocol

host otherhosts

Abstract model

Refinement to RTL level

CAMT

AB

LE

S

TAGS

RTL implementation(~30K lines of verilog)

refinement relations

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Contrast to block level verification Block verification approach to capacity problem

– isolate small blocks

– place ad hoc constraints on inputs

This is falsification because

– constraints are not verified

– block interactions not exposed to verification

Result: FV does not replace any simulation activity

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What are the implications for SMC? Verification and falsification have different needs

– Proof is as strong as its weakest link

– Hence, approximation methods are not attractive.

Importance of predictability and metrics

– Must have reliable decomposition strategies

Implications of using linear vs. branching time.

p q r s t

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Predictability Require metrics that predict model checking hardness

– Most important is number of state variables

1

0

Ver

ific

atio

n p

rob

ab

ilit

y

verification falsification # state bits

original systemreductionreduction

– Powerful MC can save steps, but is not essential

– Predictability more important than capacity

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Example -- simple pipeline

Goal: prove equivalence to unpipelined model

(modulo delay)

32 registers

+

bypass

32 bits

control

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Direct approach by model checking

Model checking completely intractable due to large number of state variables ( > 2048 )

referencemodel d

elay

pipeline

=?

ops

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Compositional refinement verification

Abstractmodel

System

Translations

11

Localized verification

Abstractmodel

System

Translations

assume prove

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Localized verification

Abstractmodel

System

Translations

assumeprove

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Circular inference rule

SPEC

1 2

: :

: :

^

( )

( )

( )

2 1

1 2

1 2

U

U

G

(related: AL 95, AH 96)

1 up to t -1 implies 2 up to t

2 up to t -1 implies 1 up to t

always 1 and 2

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Decomposition for simple pipeline

32 registers

+

32 bits

control

correct valuesfrom reference

model

1 2

1 = operand correctness

2 = result correctness

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Lemmas in SMV Operand correctness

layer L1: if(stage2.valid){ stage2.opra := stage2.aux.opra; stage2.oprb := stage2.aux.oprb; stage2.res := stage2.aux.res; }

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Effect of decomposition

Bit slicing results from "cone of influence reduction"

(similarly in reference model)

32 registers

+

32 bits

control

correct valuesfrom reference

model

1 2 1 proved

2 assumed

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Resulting MC performance Operand correctness property

0

20

40

60

80

100

120

140

0 8 16 24 32

Number of registers

Run

tim

e (s

)80 state variables

3rd order fit

Result correctness property

– easy: comparison of 32 bit adders

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NOT! Previous slide showed hand picked variable order

Actually, BDD's blow up due to bad variable ordering

– ordering based on topological distance

0

50

100

150

200

250

300

0 8 16 24 32

Number of registers

Run

tim

e (s

)

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Problem with topological ordering

Register files should be interleaved, but this is not evident from topology

bypasslogic

=?results ref. reg. file

impl. reg. file

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Sifting to the rescue (?)

Lessons (?) :

– Cannot expect to solve PSPACE problems reliably

– Need a strategy to deal with heuristic failure

1

10

100

1000

10000

0 8 16 24 32

Number of registers

Run

tim

e (s

)

Note:- Log scale- High variance

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Predictability and metrics Reducing the number of state variables

1

0

Ver

ific

atio

n p

rob

ab

ilit

y

# state bits

decomposition

– If heuristics fail, other reductions are available

2048 bits?80 bits

~600 orders of magnitude in state space size

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SPEC

P PA

Big structures and path splitting

i

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Temporal case splitting Prove separately that p holds at all times when v = i.

i G v i p

G p

: ( )*

)

Path splitting

v

record register index

G v i p( ) )

i

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Case split for simple pipeline Show only correctness for operands fetched from register i

forall(i in REG) subcase L1[i] of stage2.opra//L1 for stage2.aux.srca = i;

Abstract remaining registers to "bottom"

Result

– 23 state bits in model

– Checking one case = ~1 sec

What about the 32 cases?

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Exploiting symmetry Symmetric types

– Semantics invariant under permutations of type.

– Enforced by type checking rules.

Symmetry reduction rule

– Choose a set of representative cases under symmetry

Type REG is symmetric

– One representative case is sufficient (~1 sec)

Estimated time savings from case split: 5 orders

But wait, there's more...

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Data type reductions Problem: types with large ranges

Solution: reduce large (or infinite) types

where T\i represents all the values in T except i.

Abstract interpretation

T i T i { , \ }

i T i

i

T i

\

\ { , }

1 0

0 0 1

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Type reduction for simple pipeline Only register i is relevant

Reduce type REG to two values:

using REG->{i} prove stage2.opra//L1[i];

Number of state bits is now 11

Verification time is now independent of register file size.

Note: can also abstract out arithmetic verification using uninterpreted functions...

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Effect of reduction1

0

Ver

ific

atio

n p

rob

ab

ilit

y

# state bits

original systemreductionreduction

– Manual decomposition produces order of magnitude reductions in number of state bits

– Inflexion point in curve crossed very rapidly

20488411

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Desirata for model checking methods Importance of predictability and metrics

– Proof strategy based on reliable metric (# state bits)

– Prefer reliable performance in given range to occasional success on large problems *

e.g., stabilize variable ordering

– Methods that diverge unpredictably for small problems are less useful (e.g., infinite state, widening)

Moderate performance improvements are not that important

– Reduction steps gain multiple orders of magnitude

Approximations not appropriate

* given PSPACE completeness

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Linear v branching time Model checking v compositional verification

M | | )

fixed model for all models

Verification complexity (in formula size)

compositional

model checking

CTL LTL

linear

EXP

PSPACE

PSPACE

In practice, with LTL, we can mostly recover linear complexity...

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Avoiding "tableau variables" Problem: added state variables for LTL operators

v p X vFp Fp _Fp

Eliminating tableau variables

– Push path quantifiers inward (LTL to CTL*)

– Transition formulas (CTL+)

– Extract transition and fairness constraints

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Translating LTL to CTL* Rewrite rules

A p Ep: :

A p q Ap Aq( )^ ^

AXp AXAp

E p Ap: :

E p q Ep Eq( )_ _

EXp EXEp

In addition, if p is boolean,

E p q p Eq( )^ ^A p q p Aq( )_ _

E p q E p Eq( ) ( )U Uno rule

By adding path quantifiers, we eliminate tableau variables

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Rewrites that don't work

A p U Xq

A p U AXq

( )

( )

p p p q

q

E Xp U Xq

E Xp U EXq

( )

( )

p p

q

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Examples LTL formulas that translate to CTL formulas

G p Fq AG p AFq( ) ( )) ) (note singly nested fixed point)

G p pWq AG p A pWq( ( )) ( ( ))) )

Incomplete rewriting (to CTL*)

G p F q Xq AG p AF q Xq( ( )) ( ( ))) ^ ) ^

Note: 3 tableau variables reduced to 1

Conjecture: all resulting formulas are forward checkable

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Transition modalities Transition formulas

p Xq) v v' 1 XXq

CTL+ state modalitiesA p U q( )E p U q( ) where p is a transition formula

XAFp

Example CTL+ formulas

CTL+ still checkable in linear time

AG A p Xq( ) : ^ : :̂E p p Xp U p q( ( ) ( ))

ApEp

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Constraint extraction Extracting path constraints

A Gp q A qp( ) ( , )) where p is a transition formula

A GFp q A qGFp( ) ( ,{ })) 1

Using rewriting and above...

GFp GFq AG AFq) w/ fairness const. GFp

Circular compositional reasoning

G U

A U

) : :̂

: :̂

( ( ))

( ( ))

If and are transitionformulas, this is in CTL+, hencecomplexity is linear

Note: typically, are very large, and is small

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Effect of reducing LTL to CTL+ In practice, tableau variables rarely needed

Thus, complexity exponential only in # of state variables

– Important metric for proof strategy

Doubly nested fixed points used only where needed

– I.e., when fairness constraints apply

Forward and backward traversal possible

– Curious point: backward is commonly faster in refinement verification

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SMC for compositional verification

Cannot expect to solve PSPACE complete problems reliably

– User reductions provide fallback when heuristics fail

– Robust metrics are important to proof strategy

Each user reductions gains many orders of magnitude

– Modest performance improvements not very important

Exact verification is important

Must be able to handle linear time efficiently

BDD's are great fun, but...