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Building “Correct” Compilers. K. Vikram and S. M. Nazrul A. Outline. Introduction: Setting the high level context Motivation Detours Automated Theorem Proving Compiler Optimizations thru Dataflow Analysis Overview of the Cobalt System Forward optimizations in cobalt

Building “Correct” Compilers

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Building “Correct” Compilers

K. Vikram and S. M. Nazrul A.

- Introduction: Setting the high level context
- Motivation
- Detours
- Automated Theorem Proving
- Compiler Optimizations thru Dataflow Analysis

- Overview of the Cobalt System
- Forward optimizations in cobalt
- Proving Cobalt Optimizations Correct
- Profitability Heuristics
- Pure Analyses
- Concluding Remarks

- Introduction: Setting the high level context
- Motivation
- Detours
- Automated Theorem Proving
- Compiler Optimizations thru Dataflow Analysis

- Overview of the Cobalt System
- Forward optimizations in cobalt
- Proving Cobalt Optimizations Correct
- Profitability Heuristics
- Pure Analyses
- Concluding Remarks

Introduction

- In Vivo In Silico
- Science for Global Ubiquitous Computing
- Memories for Life
- Scalable Ubiquitous Computing Systems
- The Architecture of the Brain and Mind
- Dependable Systems Evolution
- Journeys in Non-classical computations

Introduction

- In Vivo In Silico
- Science for Global Ubiquitous Computing
- Memories for Life
- Scalable Ubiquitous Computing Systems
- The Architecture of the Brain and Mind
- Dependable Systems Evolution
- Journeys in Non-classical computations

Introduction

- A long standing problem
- Loss of financial resources, human lives

- Compare with other engineering fields!
- Non-functional requirements
- Safety, Reliability, Availability, Security, etc.

Introduction

- Was difficult so far, but now …
- Greater Technology Push
- Model checkers, theorem provers, programming theories and other formal methods

- Greater Market Pull
- Increased dependence on computing

Introduction

Building Correct Compilers

- Introduction: Setting the high level context
- Motivation
- Detours
- Automated Theorem Proving
- Compiler Optimizations thru Dataflow Analysis

- Overview of the Cobalt System
- Forward optimizations in cobalt
- Proving Cobalt Optimizations Correct
- Profitability Heuristics
- Pure Analyses
- Concluding Remarks

Motivation

- Bugs don’t manifest themselves easily
- Where is the bug – program or compiler?
- Possible solutions
- Check semantic equivalence of the two programs (translation validation, etc.)
- Prove compilers sound (manually)

- Drawbacks?
- Conservative, Difficult, Actual code not verified

DIFF

Motivation

Compiled

Prog

Source

compiler

input

output

exp-

ected

output

run!

- To get benefits, must:
- run over many inputs
- compile many test cases

- No correctness guarantees:
- neither for the compiled prog
- nor for the compiler

Semantic

DIFF

Motivation

Compiled

Prog

Source

compiler

- Translation validation
- [Pnueli et al 98, Necula 00]
- Credible compilation
- [Rinard 99]

- Compiler can still have bugs.
- Compile time increases.
- “Semantic Diff” is hard.

Correctness

checker

Motivation

Compiled

Prog

Source

compiler

compiler

Correctness

checker

Motivation

- Option 1: Prove compiler correct by hand.
- Proofs are long…
- And hard.
- Compilers are proven correct as written on paper. What about the implementation?

Correctness checker

Link?

Proof

Proof

Proof

«¬

$

\ r

t l

/ .

Motivation

Searched for “incorrect” and “wrong” in the gcc-bugs mailing list.

Some of the results:

- c/9525: incorrect code generation on SSE2 intrinsics
- target/7336: [ARM] With -Os option, gcc incorrectly computes the elimination offset
- optimization/9325: wrong conversion of constants: (int)(float)(int) (INT_MAX)
- optimization/6537: For -O (but not -O2 or -O0) incorrect assembly is generated
- optimization/6891: G++ generates incorrect code when -Os is used
- optimization/8613: [3.2/3.3/3.4 regression] -O2 optimization generates wrong code
- target/9732: PPC32: Wrong code with -O2 –fPIC
- c/8224: Incorrect joining of signed and unsigned division
- …

And this is only for February 2003!

On a mature compiler!

Motivation

compiler

- This approach: proves compiler correct automatically.

Correctness checker

Automatic

Theorem

Prover

Automatic

Theorem

Prover

The Challenge

Task of proving

compiler correct

Complexity of proving a compiler correct.

Complexity that an automatic theorem prover can handle.

- Introduction: Setting the high level context
- Motivation
- Detours
- Automated Theorem Proving
- Compiler Optimizations thru Dataflow Analysis

- Overview of the Cobalt System
- Forward optimizations in cobalt
- Proving Cobalt Optimizations Correct
- Profitability Heuristics
- Pure Analyses
- Concluding Remarks

Automated Theorem Proving

- Started with AI applications
- Reasoning about FOL sound and complete
- 1965: Unification and Resolution

- Combinatorial Explosion. SAT (NP-Complete) and FOL (decidable)
- Refinements of Resolution, Term Rewriting, Higher order Logics
- Interactive Theorem Proving
- Efficient Implementation Techniques
- Coq, Nuprl, Isabelle, Twelf, PVS, Simplify, etc.

- Introduction: Setting the high level context
- Motivation
- Detours
- Automated Theorem Proving
- Compiler Optimizations thru Dataflow Analysis

- Overview of the Cobalt System
- Forward optimizations in cobalt
- Proving Cobalt Optimizations Correct
- Profitability Heuristics
- Pure Analyses
- Concluding Remarks

Optimizations

- Optimizations are the most error prone
- Only phase that performs transformations that can potentially change semantics
- Front-end and back-end are relatively static

Optimizations

- Constant Propagation: replace constant valued variables with constants
- Common sub-expression elimination: avoid recomputing value if value has been computed earlier in the program
- Loop invariant removal: move computations into less frequently executed portions of the program
- Strength Reduction: replace expensive operations (multiplication) with simpler ones (addition)
- Dead code removal: eliminate unreachable code and code that is irrelevant to the output of the program

Optimizations

Optimizations

- Suppose x is used at program point p
- If
- on all possible execution paths from START of procedure to p
- x has constant value c at p
- then replace x by c

Optimizations

- Build the control flow graph (CFG) of the program
- Make flow of control explicit

- Perform symbolic evaluation to determine constants
- Replace constant-valued variable uses by their values and simplify expressions and control flow

Optimizations

Optimizations

- Composed of Basic Blocks
- Straight line code without any branches or merges of control flow

- Nodes of CFG
- Statements (basic blocks)/switches/merges

- Edges of CFG
- Possible control flow sequence

Optimizations

- Assign each variable the bottom value initially
- Propagate changes in variable values as statements are executed
- Based on the idea of Abstract Interpretation

Optimizations

- Flow Functions
- x := [email protected] = [email protected]{eval(e, [email protected])/x}

- Confluence Operation
- join over all incoming edges

Optimizations

- Flow Functions
- x := [email protected] = ƒ ([email protected])

- Confluence Operation
- join over all incoming edges

Optimizations

- Associate one state vector with each edge of CFG. Initialize all entries to
- Set all entries on outgoing edge from START to
- Evaluate the expression and update the output edge
- Continue till a fixed point is reached

Optimizations

Optimizations

- If each flow function ƒ is monotonic
- i.e. x ≤ y => ƒ (x) ≤ ƒ (y)

- And if the lattice is of finite height
- The dataflow algorithm terminates

Optimizations

All Paths

Any Path

Forward

Flow

Backward

Flow

- Introduction: Setting the high level context
- Motivation
- Detours
- Automated Theorem Proving
- Compiler Optimizations thru Dataflow Analysis

- Overview of the Cobalt System
- Forward optimizations in cobalt
- Proving Cobalt Optimizations Correct
- Profitability Heuristics
- Pure Analyses
- Concluding Remarks

Automatic

Theorem

Prover

Overview

Task of proving

compiler correct

Automatic

Theorem

Prover

Overview

Task of proving

optimizer correct

- Only prove optimizer correct.
- Trust front-end and code-generator.

Automatic

Theorem

Prover

Overview

Task of proving

optimizer correct

Write optimizations in Cobalt, a domain-specific language.

Automatic

Theorem

Prover

Overview

Task of proving

optimizer correct

Write optimizations in Cobalt, a domain-specific language.

Separate correctness from profitability.

Automatic

Theorem

Prover

Overview

Task of proving

optimizer correct

Write optimizations in Cobalt, a domain-specific language.

Separate correctness from profitability.

Factor out the hard and common parts of the proof, and prove them once by hand.

Overview

Interpreter

Input

Output

Cobalt Program

Overview

if (…) {

x := …;

} else {

y := …;

}

…;

Overview

Front

End

Source Code

10011011

00010100

01101101

Back

End

Binary Executable

Overview

- Cobalt language
- realistic C-like IL, operates on a CFG
- implemented const prop and folding, branch folding, CSE, PRE, DAE, partial DAE, and simple forms of points-to analyses

- Correctness checker for Cobalt opts
- using the Simplify theorem prover

- Execution engine for Cobalt opts
- in the Whirlwind compiler

Overview

Overview

- May not be able to express your opt Cobalt:
- no interprocedural optimizations for now.
- optimizations that build complicated data structures may be difficult to express.

- A sound Cobalt optimization may be rejected by the correctness checker.
- Trusted computing base (TCB) includes:
- front-end and code-generator, execution engine, correctness checker, proofs done by hand once

- Introduction: Setting the high level context
- Motivation
- Detours
- Automated Theorem Proving
- Compiler Optimizations thru Dataflow Analysis

- Overview of the Cobalt System
- Forward optimizations in cobalt
- Proving Cobalt Optimizations Correct
- Profitability Heuristics
- Pure Analyses
- Concluding Remarks

REPLACE

Forward Optimizations

y := 5

statement y := 5

statements that

don’t define y

x := y

x := 5

statement x := y

REPLACE

Forward Optimizations

if

statement y := 5

y := 5

y := 5

y := 5

is followed by

statements that

don’t define y

until

x := y

x := 5

statement x := y

then

transform statement to x := 5

Forward Optimizations

English

if

statement y := 5

is followed by

statements that

don’t define y

until

statement x := y

then

transform statement to x := 5

Forward Optimizations

Cobalt

if

statement y := 5

stmt(Y := C)

boolean expressions evaluated at nodes in the CFG

is followed by

followed by

¬ mayDef(Y)

statements that

don’t define y

until

until

statement x := y

X := Y

then

X := C

transform statement to x := 5

English version

Cobalt version

- Introduction: Setting the high level context
- Motivation
- Detours
- Automated Theorem Proving
- Compiler Optimizations thru Dataflow Analysis

- Overview of the Cobalt System
- Forward optimizations in cobalt
- Proving Cobalt Optimizations Correct
- Profitability Heuristics
- Pure Analyses
- Concluding Remarks

Proving Optimizations Correct

y := 5

y := 5

y := 5

- Witnessing region
- Invariant: y == 5

x := y

x := 5

Proving Optimizations Correct

- Ask a theorem prover to show:
- A statement satisfying stmt(Y := C) establishes Y == C
- A statement satisfying ¬mayDef(Y) maintains Y == C
- The statements X := Y and X := C have the same semantics in a program state satisfying Y == C

stmt(Y := C)

followed by

¬ mayDef(Y)

until

X := Y

X := C

with witness

Y == C

Proving Optimizations Correct

- Ask a theorem prover to show:
- A statement satisfying 1 establishes P
- A statement satisfying 2 maintains P
- The statements s and s’ have the same semantics in a program state satisfying P

1

followed by

2

until

s

s’

with witness

We showed by hand once that these conditions imply correctness.

P

- Introduction: Setting the high level context
- Motivation
- Detours
- Automated Theorem Proving
- Compiler Optimizations thru Dataflow Analysis

- Overview of the Cobalt System
- Forward optimizations in cobalt
- Proving Cobalt Optimizations Correct
- Profitability Heuristics
- Pure Analyses
- Concluding Remarks

Profitability Heuristics

- Optimization correct safe to perform any subset of the matching transformations.
- So far, all transformations were also profitable.
- In some cases, many transformations are legal, but only a few are profitable.

Profitability Heuristics

- Transformation pattern:
- defines which transformations are legal.

1

followed by

2

until

s

s’

with witness

P

filtered through

choose

- Profitability heuristic:
- describes which of the legal transformations to actually perform.
- does not affect soundness.
- can be written in a language of the user’s choice.

- This way of factoring an optimization is crucial to our ability to prove optimizations sound automatically.

Profitability Heuristics

- PRE as code duplication followed by CSE

Profitability Heuristics

- PRE as code duplication followed by CSE

a := ...;

b := ...;

if (...) {

a := ...;

x := a + b;

} else {

...

}

x := a + b;

- Code duplication

x := a + b;

Profitability Heuristics

- PRE as code duplication followed by CSE

a := ...;

b := ...;

if (...) {

a := ...;

x := a + b;

} else {

}

x :=

- Code duplication
- CSE
- self-assignment removal

x := a + b;

a + b;

x;

Profitability Heuristics

Legal placements of x := a + b

Profitable placement

a := ...;

b := ...;

if (...) {

a := ...;

x := a + b;

} else {

...

}

x := a + b;

- Introduction: Setting the high level context
- Motivation
- Detours
- Automated Theorem Proving
- Compiler Optimizations thru Dataflow Analysis

- Overview of the Cobalt System
- Forward optimizations in cobalt
- Proving Cobalt Optimizations Correct
- Profitability Heuristics
- Pure Analyses
- Concluding Remarks

Pure Analyses

- Operates on a Control Flow Graph
- A rewrite rule
- A guard to ensure appropriate conditions
- A predicate condition
- Filtered thru the choose function

Pure Analyses

- Pure analyses also possible
- Verify properties
- For use by other transformations

Pure Analyses

stmt(Y := C)

followed by

¬ mayDef(Y)

until

X := Y

X := C

with witness

Y == C

Pure Analyses

stmt(Y := C)

followed by

¬ mayDef(Y)

until

X := Y

X := C

with witness

Y == C

Pure Analyses

stmt(Y := C)

followed by

¬ mayDef(Y)

until

X := Y

X := C

with witness

- Very conservative!
- Can we do better?

Y == C

Pure Analyses

stmt(Y := C)

followed by

¬ mayDef(Y)

until

X := Y

X := C

with witness

- Very conservative!
- Can we do better?

Y == C

Pure Analyses

stmt(Y := C)

followed by

¬ mayDef(Y)

until

X := Y

X := C

with witness

Y == C

Pure Analyses

stmt(Y := C)

followed by

¬ mayDef(Y)

until

X := Y

X := C

with witness

- mayPntTo is a pure analysis.
- It computes dataflow info, but performs no transformations.

Y == C

Pure Analyses

decl X

stmt(decl X)

followed by

¬ stmt(... := &X)

defines

s

addrNotTaken(X)

with witness

mayPntTo(X,Y) ,

¬ addrNotTaken(Y)

“no location in the store points to X”

- Introduction: Setting the high level context
- Motivation
- Detours
- Automated Theorem Proving
- Compiler Optimizations thru Dataflow Analysis

- Overview of the Cobalt System
- Forward optimizations in cobalt
- Proving Cobalt Optimizations Correct
- Profitability Heuristics
- Pure Analyses
- Concluding Remarks

Concluding Remarks

- Constant propagation, folding
- Copy propagation
- Common Subexpression Elimination
- Branch Folding
- Partial Redundancy Elimination
- Loop invariant code motion
- Partial Dead Assignment Elimination

Concluding Remarks

- Improving expressiveness
- interprocedural optimizations
- one-to-many and many-to-many transformations

- Inferring the witness
- Generate specialized compiler binary from the Cobalt sources.

Concluding Remarks

- Optimizations written in a domain-specific language can be proven correct automatically.
- The correctness checker found several subtle bugs in Cobalt optimizations.
- A good step towards proving compilers correct automatically.