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  • Study Material
  • Non-Deterministic Finite Automation
    • Introduction to Compiler
    • The Structure of a Compiler
    • Intermediate Code Generation
    • Building a Compiler
    • Applications of Compiler
    • Optimizations for Computer Architectures
    • Design of New Computer Architectures
    • Program Translations
    • Software Productivity Tools
    • Programming Language Basics
    • Minimisation of DFAs
    • Explicit Access Control
    • Parameter Passing Mechanisms
    • Introduction to Lexical Analysis
    • Regular expressions
    • Short hands
    • Nondeterministic finite automata
    • Converting a regular expression to an NFA
    • Deterministic finite automata
    • Converting an NFA to a DFA
    • The subset construction
    • Dead states
    • Lexers and lexer generators
    • Splitting the input stream
    • Lexical errors
    • Properties of regular languages
    • Limits to expressive power
    • The Role of the Lexical Analyzer
    • Input Buffering
    • Specification of Tokens
    • Operations on Languages
    • Regular Definitions and Extensions
    • Recognition of Tokens
    • The Lexical-Analyzer Generator Lex
    • Finite Automata
    • Construction of an NFA from a Regular Expression
    • Efficiency of String-Processing Algorithms
    • The Structure of the Generated Analyzer
    • Optimization of DFA-Based Pattern Matchers

  • Basic Parsing Techniques
    • Introduction to Syntax analysis
    • Context-free grammars
    • Writing context free grammars
    • Derivation
    • Syntax trees and ambiguity
    • Operator precedence
    • Writing ambiguous expression grammars
    • Other sources of ambiguity
    • Syntax analysis and Predictive parsing
    • Nullable and FIRST
    • Predictive parsing revisited
    • FOLLOW
    • LL(1) parsing
    • Methods for rewriting grammars for LL(1) parsing
    • SLR parsing
    • Constructions of SLR parse tables
    • Conflicts in SLR parse-tables
    • Using precedence rules in LR parse tables
    • Using LR-parser generators
    • Properties of context-free languages
    • Introduction to Syntax-Directed Translator
    • Evaluating an SDD at the Nodes of a Parse Tree
    • Evaluation Orders for SDD\'s
    • Ordering the Evaluation of Attributes
    • A larger example of calculating FIRST and FOLLOW
    • Syntax Definition
    • Associativity of Operators
    • Parse Trees
    • Ambiguity
    • Syntax-Directed Translation
    • Synthesized Attributes
    • Tree Traversals
    • Parsing
    • Predictive Parsing
    • Use e-Productions
    • Translator for Simple Expressions
    • Semantic Rules with Controlled Side Effects
    • Applications of Syntax-Directed Translation
    • The Structure of a Type of syntax
    • Switch-Statements
    • Syntax-Directed Translation Schemes
    • Postfix Translation Schemes
    • SDT\'s With Actions Inside Productions
    • Eliminating Left Recursion from SDT\'s
    • SDT\'s for L-Attributed Definitions
    • Implementing L-Attributed SDD\'s
    • On-The-Fly Code Generation
    • L-Attributed SDD\'s and LL Parsing
    • Bottom-Up Parsing of L-Attributed SDD\'s

  • Syntax-directed Translation
    • Register Allocation and Assignment
    • Semantic Analysis
    • Introduction to Intermediate Code Generation
    • Variants of Syntax Trees
    • Variants of Syntax Trees
    • The Value-Number Method for Constructing DAG\'s
    • Three-Address Code
    • Quadruples
    • Triples
    • Static Single-Assignment Form
    • Types and Declarations
    • Type Equivalence
    • Sequences of Declarations
    • Translation of Expressions
    • Incremental Translation
    • Addressing Array Elements
    • Translation of Array References
    • Type Checking
    • Type Conversions
    • Overloading of Functions and Operators
    • Type Inference and Polymorphic Functions
    • Algorithm for Unification
    • Control Flow
    • Flow-of-Control Statements
    • Control-Flow Translation of Boolean Expressions
    • Boolean Values and Jumping Code
    • Back patching
    • Backpatching for Boolean Expressions
    • Flow-of-Control Statements
    • Break-, Continue-, and Goto-Statements
    • Introduction to Run-Time Environments
    • Stack Allocation of Space
    • Activation Records
    • Calling Sequences
    • Variable-Length Data on the Stack
    • Access to Nonlocal Data on the Stack
    • Displays
    • Heap Management
    • Locality in Programs
    • Reducing Fragmentation
    • Managing and Coalescing Free Space
    • Manual Deallocation Requests
    • Reachability
    • Introduction to Garbage Collection
    • Reference Counting Garbage Collectors
    • Introduction to Trace-Based Collection
    • Basic Abstraction
    • Optimizing Mark-and-Sweep
    • Mark-and-Compact Garbage Collectors
    • Copying collectors
    • Short-Pause Garbage Collection
    • Incremental Reachability Analysis
    • Partial-Collection Basics
    • The Train Algorithm
    • Parallel and Concurrent Garbage Collection
    • Partial Object Relocation
    • Introduction Code Generation
    • Issues in the Design of a Code Generator
    • Instruction Selection
    • Register Allocation
    • The Target Language
    • Addresses in the Target Code
    • Stack Allocation
    • Run-Time Addresses for Names
    • Basic Blocks and Flow Graphs
    • Basic Blocks
    • Next-Use Information
    • Representation of Flow Graphs
    • Optimization of Basic Blocks
    • Dead Code Elimination
    • Representation of Array References
    • Pointer Assignments and Procedure Calls
    • A Simple Code Generator
    • The Code-Generation Algorithm
    • Design of the Function getReg
    • Peephole Optimization
    • Algebraic Simplification and Reduction in Strength
    • Register Assignment for Outer Loops
    • Instruction Selection by Tree Rewriting
    • Code Generation by Tiling an Input Tree
    • Pattern Matching by Parsing
    • General Tree Matching
    • Optimal Code Generation for Expressions
    • Evaluating Expressions with an Insufficient Supply of Registers
    • Dynamic Programming Code-Generation

  • Data Flow Analysis
    • The Lazy-Code-Motion Algorithm
    • Introduction to Machine-Independent Optimizations
    • The Dynamic Programming Algorithm
    • The Principal Sources of Optimization
    • Semantics-Preserving Transformations
    • Copy Propagation
    • Induction Variables and Reduction in Strength
    • Introduction to Data-Flow Analysis
    • The Data-Flow Analysis Schema
    • Reaching Definitions
    • Live-Variable Analysis
    • Available Expressions
    • Foundations of Data-Flow Analysis
    • Transfer Functions
    • The Iterative Algorithm for General Frameworks
    • Meaning of a Data-Flow Solution
    • Constant Propagation
    • Transfer Functions for the Constant-Propagation Framework
    • Partial-Redundancy Elimination
    • The Lazy-Code-Motion Problem
    • Loops in Flow Graphs
    • Depth-First Ordering
    • Back Edges and Reducibility
    • Natural Loops
    • Speed of Convergence of Iterative Data-Flow Algorithms
    • Region-Based Analysis
    • Necessary Assumptions About Transfer Functions
    • An Algorithm for Region-Based Analysis
    • Handling Non-reducible Flow Graphs
    • Symbolic Analysis
    • Data-Flow Problem Formulation
    • Region-Based Symbolic Analysis

  • Code Generation
    • Introduction to Software Pipelining of Loops
    • Matrix Multiply: An In-Depth Example
    • Software Pipelining of Loops
    • Introduction Instruction-Level Parallelism
    • Multiple Instruction Issue
    • A Basic Machine Model
    • Code-Scheduling Constraints
    • Finding Dependences Among Memory Accesses
    • Phase Ordering Between Register Allocation and Code Scheduling
    • Speculative Execution Support
    • Basic-Block Scheduling
    • List Scheduling of Basic Blocks
    • Global Code Scheduling
    • Upward Code Motion
    • Updating Data Dependences
    • Advanced Code Motion Techniques
    • Software Pipelining
    • Register Allocation and Code Generation
    • A Software-Pipelining Algorithm
    • Scheduling Cyclic Dependence Graphs
    • Improvements to the Pipelining Algorithms
    • Conditional Statements and Hardware Support for Software Pipelining
    • Basic Concepts of Parallelism and Locality
    • Parallelism in Applications
    • Loop-Level Parallelism
    • Introduction to Affine Transform Theory
    • Optimizations
    • Iteration Spaces
    • Affine Array Indexes
    • Controlling the Order of Execution
    • Changing Axes
    • Intermediate Code for Procedures
    • Data Reuse
    • Self Reuse
    • Self-Spatial Reuse
    • Array Data-Dependence Analysis
    • Integer Linear Programming
    • Heuristics for Solving Integer Linear Programs
    • Solving General Integer Linear Programs
    • Finding Synchronization-Free Parallelism
    • Affine Space Partitions
    • Space-Partition Constraints
    • Solving Space-Partition Constraints
    • A Simple Code-Generation Algorithm
    • Eliminating Empty Iterations
    • Synchronization Between Parallel Loops
    • The Parallelization Algorithm and Hierarchical Time
    • Pipelining
    • Solving Time-Partition Constraints by Farkas' Lemma
    • Code Transformations
    • Parallelism With Minimum Synchronization
    • Locality Optimizations
    • Partition Interleaving
    • Putting it All Together
    • Uses of Affine Transforms
    • Interprocedural Analysis
    • Context Sensitivity
    • Cloning-Based Context-Sensitive Analysis
    • Importance of Interprocedural Analysis
    • SQL Injection
    • A Logical Representation of Data Flow
    • Execution of Datalog Programs
    • Problematic Datalog Rules
    • A Simple Pointer-Analysis Algorithm
    • Flow Insensitivity
    • Context-Insensitive Interprocedural Analysis
    • Context-Sensitive Pointer Analysis
    • Adding Context to Datalog Rules
    • Datalog Implementation by BDD's
    • Relational Operations as BDD Operations

Branch : Computer Science and Engineering
Subject : Compiler design
Unit : Syntax-directed Translation

Introduction to Run-Time Environments


Introduction: Run-Time structure is to hold State regarding the execution of a procedure. Run-time mainly deals with the actions that must occur to implement the program. The allocation and de-allocation of data objects is managed by the run-time support package, the representation of data objects at run-time is determined by its type.

A compiler must accurately implement the abstractions embodied in the source language definition, such as names, scopes, bindings, data types, operators, procedures, parameters, and flow-of-control constructs. The compiler must cooperate with the operating system and other systems software to support these abstractions on the target machine.

To do so, the compiler creates and manages a run-time environment in which it assumes its target programs are being executed. This environment deals with a variety of issues such as the layout and allocation of storage locations for the objects named in the source program, the mechanisms used by the target program to access variables, the linkages between procedures, the mechanisms for passing parameters, and the interfaces to the operating system, input/output devices, and other programs.

Storage Organization: From the perspective of the compiler writer, the executing target program runsin its own logical address space in which each program value has a location. Themanagement and organization of this logical address space is shared betweenthe compiler, operating system, and target machine. The operating systemmaps the logical addresses into physical addresses, which are usually spreadthroughout memory.

The run-time representation of an object program in the logical address space consists of data and program areas as shown in Fig. A compiler for a language like C on an operating system like Linux might subdivide memory in this way.

Figure: Typical subdivision of run-time memory into code and data areas

The storage layout for data objects is strongly influenced by the addressing constraints of the target machine. On many machines, instructions to add integers may expect integers to be aligned, that is, placed at an address divisible by 4. Although an array of ten characters needs only enough bytes to hold ten characters, a compiler may allocate 12 bytes to get the proper alignment, leaving 2 bytes unused. Space left unused due to alignment considerations is referred to as padding. When space is at a premium, a compiler may pack data so that no padding is left; additional instructions may then need to be executed at run time to position packed data so that it can be operated on as if it were properly aligned.

The size of the generated target code is fixed at compile time, so the compiler can place the executable target code in a statically determined area Code, usually in the low end of memory. Similarly, the size of some program data objects, such as global constants, and data generated by the compiler, such as information to support garbage collection, may be known at compile time, and these data objects can be placed in another statically determined area called Static. One reason for statically allocating as many data objects as possible is that the addresses of these objects can be compiled into the target code. In early versions of Fortran, all data objects could be allocated statically.

Static Versus Dynamic Storage Allocation: The layout and allocation of data to memory locations in the run-time environment are key issues in storage management. These issues are tricky because the same name in a program text can refer to multiple locations at run time.

The two adjectives static and dynamic distinguish between compile time and run time, respectively. We say that a storage-allocation decision is static, if it can be made by the compiler looking only at the text of the program, not at what the program does when it executes. Conversely, a decision is dynamic if it can be decided only while the program is running. Many compilers use some combination of the following two strategies for dynamic storage allocation:

Stack storage. Names local to a procedure are allocated space on a stack. The stack supports the normal call/return policy for procedures.

Heap storage. Data that may outlive the call to the procedure that created it is usually allocated on a "heap" of reusable storage. The heap is an area of virtual memory that allows objects or other data elements to obtain storage when they are created and to return that storage when they are invalidate.

To support heap management, "garbage collection" enables the run-time system to detect useless data elements and reuse their storage, even if the programmer does not return their space explicitly. Automatic garbage collection is an essential feature of many modern languages, despite it being a difficult operation to do efficiently; it may not even be possible for some languages.   

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