CS 406/534 Compiler Construction Putting It All...

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CS 406/534 Compiler ConstructionPutting It All Together

Prof. Li XuDept. of Computer Science

UMass LowellFall 2004

Part of the course lecture notes are based on Prof. Keith Cooper, Prof. Ken Kennedy and Dr. Linda Torczon’s teaching materials at Rice University.

All rights reserved.

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CS406/534 Fall 2004, Prof. Li Xu 2

AdministraviaLast lecture todayLab2 and lab3 graded Final exam now handed out, due 12/20

Deadline is firm: late exam will not be graded

Extra credit lab3 presentation today

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What We Did Last TimeProgram analysis and optimization

Overview of compiler optimizationLocal optimization

DAGValue numbering

Control flow analysisCFG, DOM tree, natural loops

Data flow analysisGeneric frameworkTypical data flow problems

AVAIL, REACH, LIVE

SSA

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Today’s GoalsSummary of the subjects we’ve coveredPerspectives and final remarks

How will you use 91.406/534 knowledge?

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High-level View

DefinitionsCompiler consumes source code & produces target code

usually translate high-level language programs into machine codeInterpreter consumes executables & produces results

virtual machine for the input code

Sourcecode

MachinecodeCompiler

Errors

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Why Study Compilers?Compilers are important

Enabling technology for languages, software developmentAllow programmers to focus on problem solving, hiding the hardware complexityResponsible for good system performance

Compilers are usefulLanguage processing is broadly applicable

Compilers are funCombine theory and practiceOverlap with other CS subjectsHard problemsEngineering and trade-offsGot a taste in the labs!

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Structure of Compilers

Front Middle End Back End

Infrastructure: symbol tables, trees, graphs, intermediate representations, sets, tuples

Scanner

Parser

CS

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Optim

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Instruction Selection

Instruction Scheduling

Register A

llocation

IRIR

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The Front-end



Scanner

Parser

CS

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Register A

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IRIR

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Lexical AnalysisScanner

Maps character stream into tokens Automate scanner construction

Define tokens using Regular ExpressionsConstruct NFA (Nondeterministic Finite Automata) to recognize REsTransform NFA to DFA

Convert NFA to DFA through subset constructionDFA minimization (set split)

Building scanners from DFATools

ANTLR, lex

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Syntax AnalysisParsing language using CFG (context-free grammar)CFG grammar theory

DerivationParse treeGrammar ambiguity

Parsing Top-down parsing

recursive descenttable-driven LL(1)

Bottom-up parsingLR(1) shift reduce parsing

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Top-down Predictive ParsingBasic ideaBuild parse tree from root. Given A → α | β, use look-ahead symbol to choose between α & β

Recursive descentTable-driven LL(1)

Left recursion elimination

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Bottom-up Shift-Reduce ParsingBuild reverse rightmost derivation

The key is to find handle (rhs of production)

All active handles include top of stack (TOS)Shift inputs until TOS is right end of a handle

Language of handles is regular (finite)Build a handle-recognizing DFAACTION & GOTO tables encode the DFA

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Semantic AnalysisAnalyze context and semantics

types and other semantic checks

Attribute grammarassociate evaluation rules with grammar production

Ad-hoc build symbol table

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Intermediate Representation



Scanner

Parser

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Register A

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IRIR

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Intermediate RepresentationFront-end translates program into IR format for further analysis and optimization

IR encodes the compiler’s knowledge of the programLargely machine-independentMove closer to standard machine model

AST Tree: high-levelLinear IR: low-level

ILOC 3-address codeAssembly-level operationsExpose control flow, memory addressingunlimited virtual registers

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Procedure AbstractionProcedure is key language construct for building large systems

Name SpaceCaller-callee interface: linkage convention

Control transferContext protectionParameter passing and return value

Run-time support for nested scopesActivation record, access link, display

Inheritance and dynamic dispatch for OOmultiple inheritancevirtual method table

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The Back-end



Scanner

Parser

CS

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Optim

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Optim

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Register A

llocation

IRIR

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The Back-endInstruction selection

Mapping IR into assembly codeAssumes a fixed storage mapping & code shapeCombining operations, using address modes

Instruction schedulingReordering operations to hide latenciesAssumes a fixed program (set of operations)Changes demand for registers

Register allocationDeciding which values will reside in registersChanges the storage mapping, may add false sharingConcerns about placement of data & memory operations

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Code GenerationExpressions

Recursive tree walk on ASTDirect integration with parser

AssignmentArray referenceBoolean & Relational ValuesIf-then-elseCaseLoopProcedure call

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Instruction SelectionHand-coded tree-walk code generatorAutomatic instruction selection

Pattern matchingPeephole MatchingTree-pattern matching through tiling

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Instruction SchedulingThe Problem

Given a code fragment for some target machine and the latencies for each individual operation, reorder the operationsto minimize execution time

Build Precedence GraphList scheduling

NP-complete problemHeuristics work well for basic blocks

forward list schedulingbackward list scheduling

Scheduling for larger regionsEBB and cloningTrace scheduling

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Register AllocationLocal register allocation

top-downbottom-up

Global register allocationFind live-rangeBuild an interference graph GI

Construct a k-coloring of interference graphMap colors onto physical registers

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Web-based Live Ranges

def x def x

def y

use xuse x

use y def x

use x

use y

def yl1

l2

l3

l4

Connect common defs and usesSolve the Reaching data-flow problem!

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Interference GraphThe interference graph, GI

Nodes in GI represent live rangesEdges in GI represent individual interferences

For x, y ∈ GI, <x,y> ∈ iff x and y interfere

A k-coloring of GI can be mapped into an allocation to k registers

3-colorable

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Key Observation on ColoringAny vertex n that has fewer than kneighbors in the interference graph (n° < k) can always be colored !Remove nodes n° < k for GI ’, coloring for GI ’ is also coloring for GI .

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Chaitin’s Algorithm1. While ∃ vertices with < k neighbors in GI

> Pick any vertex n such that n°< k and put it on the stack> Remove that vertex and all edges incident to it from GI

• This will lower the degree of n’s neighbors

2. If GI is non-empty (all vertices have k or more neighbors) then:> Pick a vertex n (using some heuristic) and spill the live range

associated with n> Remove vertex n from GI , along with all edges incident to it and put it

on the stack> If this causes some vertex in GI to have fewer than k neighbors, then

go to step 1; otherwise, repeat step 2

3. If no spill, successively pop vertices off the stack and color them in the lowest color not used by some neighbor; otherwise, insert spill code, recompute GI and start from step 1

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Brigg’s ImprovementNodes can still be colored even with > k neighbors if some

neighbors have same color

1. While ∃ vertices with < k neighbors in GI > Pick any vertex n such that n°< k and put it on the stack> Remove that vertex and all edges incident to it from GI

• This may create vertices with fewer than k neighbors

2. If GI is non-empty (all vertices have k or more neighbors) then:> Pick a vertex n (using some heuristic condition), push n on the stack

and remove n from GI , along with all edges incident to it> If this causes some vertex in GI to have fewer than k neighbors, then

go to step 1; otherwise, repeat step 2

3. Successively pop vertices off the stack and color them in the lowest color not used by some neighbor

> If some vertex cannot be colored, then pick an uncolored vertex to spill, spill it, and restart at step 1

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The Middle-end: Optimizer



Scanner

Parser

CS

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Register A

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IRIR

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Principles of Compiler Optimizationsafety

Does applying the transformation change the results of executing the code?

profitabilityIs there a reasonable expectation that applying the transformation will improve the code?

opportunityCan we efficiently and frequently find places to apply optimization

Optimizing compilerProgram AnalysisProgram Transformation

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Program AnalysisControl-flow analysisData-flow analysis

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Control Flow AnalysisBasic blocksControl flow graphDominator treeNatural loopsDominance frontier

the join points for SSAinsert Ф node

CFG DOM Tree

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Data Flow Analysis“compile-time reasoning about the run-time flow of values”

represent effects of each basic blockpropagate facts around control flow graph

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DFA: The Big PictureSet up a set of equations that relate program properties at different program points in terms of the properties at "nearby" program points

B

IN(B)

OUT(B)

local(B)

Transfer functionForward analysis: compute OUT(B) in terms IN(B)

Available expressionsReaching definition

Backward analysis: compute IN(B) in terms of OUT(B)

Variable livenessVery busy expressions

Meet function for join pointsForward analysis: combine OUT(p) of predecessors to form IN(B)Backward analysis: combine IN(s) of successors to form OUT(B)

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Available ExpressionBasic block b

IN(b): expressions available at b’s entryOUT(b): expressions available at b’s exitLocal sets

: expressions defined in b and available on exit: expressions killed in b

An expression is killed in b if operands are assigned in b

Transfer function

Meet function

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More Data Flow ProblemsAVAIL Equations

More data flow problemsReaching DefinitionLiveness

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Compiler OptimizationLocal optimization

DAG CSEValue numbering

Global optimization enabled by DFAGlobal CSE (AVAIL)Constant propagation (Def-Use)Dead code elimination (Use-Def)

Advanced topic: SSA

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PerspectiveFront End Middle End Back End


Scanner

Parser

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Analysis



Register A

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Front end: essentially solved problemMiddle end: domain-specific languageBack end: new architectureVerifying compiler, reliability, security

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Interesting Stuff We SkippedInterprocedural analysisAlias (pointer) analysisGarbage collection

Check the literature reference in EaC

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How will you use 91.406/534 knowledge?As informed programmer As informed small language designerAs informed hardware engineerAs compiler writer

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Informed Programmer“Knowledge is power”

Compiler is no longer a black boxKnow how compiler works

ImplicationsUse of language features

Avoid those can cause problemGive compiler hints

Code optimizationDon’t optimize prematurelyDon’t write complicated code

DebuggingUnderstand the compiled code

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Solving Problem the Compiler WaySolve problems from language/compiler perspective

Implement simple languageExtend language

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Informed Hardware EngineerCompiler support for programmable hardware

pervasive computingnew back-ends for new processors

Design new architectureswhat can compiler do and not dohow to expose and use compiler to manage hardware resources

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Compiler WriterMake a living by writing compilers!

TheoryAlgorithmsEngineering

We have built:scannerparserAST tree builder, type checkerregister allocatorinstruction scheduler

Used compiler generation toolsANTLR, lex, yacc, etc

On track to jump into compiler development!

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Final RemarksCompiler construction

TheoryImplementation

How to use what you learned in 91.406/534?As informed programmer As informed small language designerAs informed hardware engineerAs compiler writer

… and live happily ever after

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CS 406/534 Compiler Construction Putting It All...

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