Compiler Construction - PowerPoint PPT Presentation

About This Presentation
Title:

Compiler Construction

Description:

Title: ITS 015: Compiler Construction Author: os Last modified by: user Created Date: 8/30/2005 7:14:21 AM Document presentation format: – PowerPoint PPT presentation

Number of Views:370
Avg rating:3.0/5.0
Slides: 43
Provided by: OS7
Category:

less

Transcript and Presenter's Notes

Title: Compiler Construction


1
Compiler Construction
  • Overview

2
Todays Goals
  • Summary of the subjects weve covered
  • Perspectives and final remarks

3
High-level View
  • Definitions
  • Compiler consumes source code produces target
    code
  • usually translate high-level language programs
    into machine code
  • Interpreter consumes executables produces
    results
  • virtual machine for the input code

4
Why Study Compilers?
  • Compilers are important
  • Enabling technology for languages, software
    development
  • Allow programmers to focus on problem solving,
    hiding the hardware complexity
  • Responsible for good system performance
  • Compilers are useful
  • Language processing is broadly applicable
  • Compilers are fun
  • Combine theory and practice
  • Overlap with other CS subjects
  • Hard problems
  • Engineering and trade-offs
  • Got a taste in the labs!

5
Structure of Compilers
6
The Front-end
7
Lexical Analysis
  • Scanner
  • Maps character stream into tokens
  • Automate scanner construction
  • Define tokens using Regular Expressions
  • Construct NFA (Nondeterministic Finite Automata)
    to recognize REs
  • Transform NFA to DFA
  • Convert NFA to DFA through subset construction
  • DFA minimization (set split)
  • Building scanners from DFA
  • Tools
  • ANTLR, lex

8
Syntax Analysis
  • Parsing language using CFG (context-free grammar)
  • CFG grammar theory
  • Derivation
  • Parse tree
  • Grammar ambiguity
  • Parsing
  • Top-down parsing
  • recursive descent
  • table-driven LL(1)
  • Bottom-up parsing
  • LR(1) shift reduce parsing
  • Operator precedence parsing

9
Top-down Predictive Parsing
  • Basic idea
  • Build parse tree from root. Given A ? a ß,use
    look-ahead symbol to choose between a ß
  • Recursive descent
  • Table-driven LL(1)
  • Left recursion elimination

10
Bottom-up Shift-Reduce Parsing
  • Build 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 DFA
  • ACTION GOTO tables encode the DFA

11
Semantic Analysis
  • Analyze context and semantics
  • types and other semantic checks
  • Attribute grammar
  • associate evaluation rules with grammar
    production
  • Ad-hoc
  • build symbol table

12
Intermediate Representation
13
Intermediate Representation
  • Front-end translates program into IR format for
    further analysis and optimization
  • IR encodes the compilers knowledge of the
    program
  • Largely machine-independent
  • Move closer to standard machine model
  • AST Tree high-level
  • Linear IR low-level
  • ILOC 3-address code
  • Assembly-level operations
  • Expose control flow, memory addressing
  • unlimited virtual registers

14
Procedure Abstraction
  • Procedure is key language construct for building
    large systems
  • Name Space
  • Caller-callee interface linkage convention
  • Control transfer
  • Context protection
  • Parameter passing and return value
  • Run-time support for nested scopes
  • Activation record, access link, display
  • Inheritance and dynamic dispatch for OO
  • multiple inheritance
  • virtual method table

15
The Back-end
16
The Back-end
  • Instruction selection
  • Mapping IR into assembly code
  • Assumes a fixed storage mapping code shape
  • Combining operations, using address modes
  • Instruction scheduling
  • Reordering operations to hide latencies
  • Assumes a fixed program (set of operations)
  • Changes demand for registers
  • Register allocation
  • Deciding which values will reside in registers
  • Changes the storage mapping, may add false
    sharing
  • Concerns about placement of data memory
    operations

17
Code Generation
  • Expressions
  • Recursive tree walk on AST
  • Direct integration with parser
  • Assignment
  • Array reference
  • Boolean Relational Values
  • If-then-else
  • Case
  • Loop
  • Procedure call

18
Instruction Selection
  • Hand-coded tree-walk code generator
  • Automatic instruction selection
  • Pattern matching
  • Peephole Matching
  • Tree-pattern matching through tiling

19
Instruction Scheduling
  • The Problem
  • Given a code fragment for some target machine and
    the
  • latencies for each individual operation, reorder
    the operations
  • to minimize execution time
  • Build Precedence Graph
  • List scheduling
  • NP-complete problem
  • Heuristics work well for basic blocks
  • forward list scheduling
  • backward list scheduling
  • Scheduling for larger regions
  • EBB and cloning
  • Trace scheduling

20
Register Allocation
  • Local register allocation
  • top-down
  • bottom-up
  • Global register allocation
  • Find live-range
  • Build an interference graph GI
  • Construct a k-coloring of interference graph
  • Map colors onto physical registers

21
Web-based Live Ranges
  • Connect common defs and uses
  • Solve the Reaching data-flow problem!

22
Interference Graph
  • The interference graph, GI
  • Nodes in GI represent live ranges
  • Edges in GI represent individual interferences
  • For x, y ? GI, ltx,ygt ? iff x and y interfere
  • A k-coloring of GI can be mapped into an
  • allocation to k registers

23
Key Observation on Coloring
  • Any vertex n that has fewer than k neighbors in
    the interference graph (nlt k) can always be
    colored !
  • Remove nodes nlt k for GI , coloring for GI is
    also coloring for GI

24
Chaitins Algorithm
  • While ? vertices with lt k neighbors in GI
  • Pick any vertex n such that nlt k and put it on
    the stack
  • Remove that vertex and all edges incident to it
    from GI
  • This will lower the degree of ns neighbors
  • 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
  • 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

25
Briggs Improvement
  • Nodes can still be colored even with gt k
    neighbors if some neighbors have same color
  • While ? vertices with lt k neighbors in GI
  • Pick any vertex n such that nlt 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
  • 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
  • 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

26
The Middle-end Optimizer
27
Principles of Compiler Optimization
  • safety
  • Does applying the transformation change the
    results of executing the code?
  • profitability
  • Is there a reasonable expectation that applying
    the transformation will improve the code?
  • opportunity
  • Can we efficiently and frequently find places to
    apply optimization
  • Optimizing compiler
  • Program Analysis
  • Program Transformation

28
Program Analysis
  • Control-flow analysis
  • Data-flow analysis

29
Control Flow Analysis
  • Basic blocks
  • Control flow graph
  • Dominator tree
  • Natural loops
  • Dominance frontier
  • the join points for SSA
  • insert ? node

30
Data Flow Analysis
  • compile-time reasoning about the runtime flow of
    values
  • represent effects of each basic block
  • propagate facts around control flow graph

31
DFA The Big Picture
  • Set up a set of equations that relate program
    properties at different program points in terms
    of the properties at "nearby" program points
  • Transfer function
  • Forward analysis compute OUT(B) in terms IN(B)
  • Available expressions
  • Reaching definition
  • Backward analysis compute IN(B) in terms of
    OUT(B)
  • Variable liveness
  • Very busy expressions
  • Meet function for join points
  • Forward analysis combine OUT(p) of predecessors
    to form IN(B)
  • Backward analysis combine IN(s) of successors to
    form OUT(B)

32
Available Expression
  • Basic block b
  • IN(b) expressions available at bs entry
  • OUT(b) expressiongs available at bs exit
  • Local sets
  • def(b) expressions defined in b and available on
    exit
  • killed(b) expressions killed in b
  • An expression is killed in b if operands are
    assigned in b
  • Transfer function
  • OUT(b) def(b) ? (IN(b) killed(b))
  • Meet function
  • IN(b)

33
More Data Flow Problems
  • AVAIL Equations
  • More data flow problems
  • Reaching Definition
  • Liveness

meet function ? n
forward reaching definition available expression
backward variable liveness very busy expression
34
Compiler Optimization
  • Local optimization
  • DAG CSE
  • Value numbering
  • Global optimization enabled by DFA
  • Global CSE (AVAIL)
  • Constant propagation (Def-Use)
  • Dead code elimination (Use-Def)
  • Advanced topic SSA

35
Perspective
  • Front end essentially solved problem
  • Middle end domain-specific language
  • Back end new architecture
  • Verifying compiler, reliability, security

36
Interesting Stuff We Skipped
  • Interprocedural analysis
  • Alias (pointer) analysis
  • Garbage collection
  • Check the literature reference in EaC

37
How will you use the knowledge?
  • As informed programmer
  • As informed small language designer
  • As informed hardware engineer
  • As compiler writer

38
Informed Programmer
  • Knowledge is power
  • Compiler is no longer a black box
  • Know how compiler works
  • Implications
  • Use of language features
  • Avoid those can cause problem
  • Give compiler hints
  • Code optimization
  • Dont optimize prematurely
  • Dont write complicated code
  • Debugging
  • Understand the compiled code

39
Solving Problem the Compiler Way
  • Solve problems from language/compiler perspective
  • Implement simple language
  • Extend language

40
Informed Hardware Engineer
  • Compiler support for programmable hardware
  • pervasive computing
  • new back-ends for new processors
  • Design new architectures
  • what can compiler do and not do
  • how to expose and use compiler to manage hardware
    resources

41
Compiler Writer
  • Make a living by writing compilers!
  • Theory
  • Algorithms
  • Engineering
  • We have built
  • scanner
  • parser
  • AST tree builder, type checker
  • register allocator
  • instruction scheduler
  • Used compiler generation tools
  • ANTLR, lex, yacc, etc

On track to jump into compiler development!
42
Final Remarks
  • Compiler construction
  • Theory
  • Implementation
  • How to use what you learned in this lecture?
  • As informed programmer
  • As informed small language designer
  • As informed hardware engineer
  • As compiler writer
  • and live happily ever after
Write a Comment
User Comments (0)
About PowerShow.com