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From the PublisherEngineering a Compiler is a rich survey and exposition of the important techniques necessary to build a modern compiler.
-Jim Larus, Microsoft Research
The proliferation of processors, environments, and constraints on systems has cast compiler technology into a wider variety of settings, changing the compiler and compiler writer's role. No longer is execution speed the sole criterion for judging compiled code. Today, code might be judged on how small it is, how much power it consumes, how well it compresses, or how many page faults it generates. In this evolving environment, the task of building a successful compiler relies upon the compiler writer's ability to balance and blend algorithms, engineering insights, and careful planning. Today's compiler writer must choose a path through a design space that is filled with diverse alternatives, each with distinct costs, advantages, and complexities.
Engineering a Compiler explores this design space by presenting some of the ways these problems have been solved, and the constraints that made each of those solutions attractive. By understanding the parameters of the problem and their impact on compiler design, the authors hope to convey both the depth of the problems and the breadth of possible solutions. Their goal is to cover a broad enough selection of material to show readers that real tradeoffs exist, and that the impact of those choices can be both subtle and far-reaching.
Authors Keith Cooper and Linda Torczon convey both the art and the science of compiler construction and show best practice algorithms for the major passes of a compiler. Their text re-balances the curriculum for an introductory course in compiler construction to reflect the issues that arise in current practice.
·Focuses on the back end of the compiler—reflecting the focus of research and development over the last decade.
·Uses the well-developed theory from scanning and parsing to introduce concepts that play a critical role in optimization and code generation.
·Introduces the student to optimization through data-flow analysis, SSA form, and a selection of scalar optimizations.
·Builds on this background to teach modern methods in code generation: instruction selection, instruction scheduling, and register allocation.
·Presents examples in several different programming languages in order to best illustrate the concept.
·Provides end-of-chapter exercises, with on-line solutions available to instructors.
Audience: Undergraduate Computer Science and Computer Engineering majors.
* CHAPTER OVERVIEW
Compilers are computer programs that translate a program written in one language into a program written in another language. At the same time, a compiler is a large software system, with many internal components and algorithms and complex interactions between them. Thus, the study of compiler construction is an introduction to techniques for the translation and improvement of programs, and a practical exercise in software engineering. This chapter provides a conceptual overview of all the major components of a modern compiler.
Keywords: Compiler, Interpreter, Automatic Translation
The role of the computer in daily life grows each year. With the rise of the Internet, computers and the software that runs on them provide communications, news, entertainment, and security. Embedded computers have changed the ways that we build automobiles, airplanes, telephones, televisions, and radios. Computation has created entirely new categories of activity, from video games to social networks. Supercomputers predict daily weather and the course of violent storms. Embedded computers synchronize traffic lights and deliver e-mail to your pocket.
All of these computer applications rely on software computer programs that build virtual tools on top of the low-level abstractions provided by the underlying hardware. Almost all of that software is translated by a tool called a compiler. A compiler is simply a computer program that translates other computer programs to prepare them for execution. This book presents the fundamental techniques of automatic translation that are used to build compilers. It describes many of the challenges that arise in compiler construction and the algorithms that compiler writers use to address them.
A compiler is a tool that translates software written in one language into another language. To translate text from one language to another, the tool must understand both the form, or syntax, and content, or meaning, of the input language. It needs to understand the rules that govern syntax and meaning in the output language. Finally, it needs a scheme for mapping content from the source language to the target language.
The structure of a typical compiler derives from these simple observations. The compiler has a front end to deal with the source language. It has a back end to deal with the target language. Connecting the front end and the back end, it has a formal structure for representing the program in an intermediate form whose meaning is largely independent of either language. To improve the translation, a compiler often includes an optimizer that analyzes and rewrites that intermediate form.
Computer programs are simply sequences of abstract operations written in a programming language—a formal language designed for expressing computation. Programming languages have rigid properties and meanings—as opposed to natural languages, such as Chinese or Portuguese. Programming languages are designed for expressiveness, conciseness, and clarity. Natural languages allow ambiguity. Programming languages are designed to avoid ambiguity; an ambiguous program has no meaning. Programming languages are designed to specify computations—to record the sequence of actions that perform some task or produce some results.
Programming languages are, in general, designed to allow humans to express computations as sequences of operations. Computer processors, hereafter referred to as processors, microprocessors, or machines, are designed to execute sequences of operations. The operations that a processor implements are, for the most part, at a much lower level of abstraction than those specified in a programming language. For example, a programming language typically includes a concise way to print some number to a file. That single programming language statement must be translated into literally hundreds of machine operations before it can execute.
The tool that performs such translations is called a compiler. The compiler takes as input a program written in some language and produces as its output an equivalent program. In the classic notion of a compiler, the output program is expressed in the operations available on some specific processor, often called the target machine. Viewed as a black box, a compiler might look like this:
Typical "source" languages might be C, C++, FORTRAN, Java, or ml. The "target" language is usually the instruction set of some processor.
Some compilers produce a target program written in a human-oriented programming language rather than the assembly language of some computer. The programs that these compilers produce require further translation before they can execute directly on a computer. Many research compilers produce C programs as their output. Because compilers for C are available on most computers, this makes the target program executable on all those systems, at the cost of an extra compilation for the final target. Compilers that target programming languages rather than the instruction set of a computer are often called source-to-source translators.
Many other systems qualify as compilers. For example, a typesetting program that produces PostScript can be considered a compiler. It takes as input a specification for how the document should look on the printed page and it produces as output a PostScript file. PostScript is simply a language for describing images. Because the typesetting program takes an executable specification and produces another executable specification, it is a compiler.
The code that turns PostScript into pixels is typically an interpreter, not a compiler. An interpreter takes as input an executable specification and produces as output the result of executing the specification.
Some languages, such as Perl, Scheme, and apl, are more often implemented with interpreters than with compilers.
Some languages adopt translation schemes that include both compilation and interpretation. Java is compiled from source code into a form called bytecode, a compact representation intended to decrease download times for Java applications. Java applications execute by running the bytecode on the corresponding Java Virtual Machine (JVM), an interpreter for bytecode. To complicate the picture further, many implementations of the JVM include a compiler that executes at runtime, sometimes called a just-in-time compiler, or JIT, that translates heavily used bytecode sequences into native code for the underlying computer.
Interpreters and compilers have much in common. They perform many of the same tasks. Both analyze the input program and determine whether or not it is a valid program. Both build an internal model of the structure and meaning of the program. Both determine where to store values during execution. However, interpreting the code to produce a result is quite different from emitting a translated program that can be executed to produce the result. This book focuses on the problems that arise in building compilers. However, an implementor of interpreters may find much of the material relevant.
Why Study Compiler Construction?
A compiler is a large, complex program. Compilers often include hundreds of thousands, if not millions, of lines of code, organized into multiple subsystems and components. The various parts of a compiler interact in complex ways. Design decisions made for one part of the compiler have important ramifications for other parts. Thus, the design and implementation of a compiler is a substantial exercise in software engineering.
A good compiler contains a microcosm of computer science. It makes practical use of greedy algorithms (register allocation), heuristic search techniques (list scheduling), graph algorithms (dead-code elimination), dynamic programming (instruction selection), finite automata and push-down automata (scanning and parsing), and fixed-point algorithms (data-flow analysis). It deals with problems such as dynamic allocation, synchronization, naming, locality, memory hierarchy management, and pipeline scheduling. Few software systems bring together as many complex and diverse components. Working inside a compiler provides practical experience in software engineering that is hard to obtain with smaller, less intricate systems.
Compilers play a fundamental role in the central activity of computer science: preparing problems for solution by computer. Most software is compiled, and the correctness of that process and the efficiency of the resulting code have a direct impact on our ability to build large systems. Most students are not satisfied with reading about these ideas; many of the ideas must be implemented to be appreciated. Thus, the study of compiler construction is an important component of a computer science education.
Compilers demonstrate the successful application of theory to practical problems. The tools that automate the production of scanners and parsers apply results from formal language theory. These same tools are used for text searching, website filtering, word processing, and command-language interpreters. Type checking and static analysis apply results from lattice theory, number theory, and other branches of mathematics to understand and improve programs. Code generators use algorithms for tree-pattern matching, parsing, dynamic programming, and text matching to automate the selection of instructions.
Still, some problems that arise in compiler construction are open problems—that is, the current best solutions have room for improvement. Attempts to design high-level, universal, intermediate representations have foundered on complexity. The dominant method for scheduling instructions is a greedy algorithm with several layers of tie-breaking heuristics. While it is obvious that compilers should use commutativity and associativity to improve the code, most compilers that try to do so simply rearrange the expression into some canonical order.
Building a successful compiler requires expertise in algorithms, engineering, and planning. Good compilers approximate the solutions to hard problems. They emphasize efficiency, in their own implementations and in the code they generate. They have internal data structures and knowledge representations that expose the right level of detail—enough to allow strong optimization, but not enough to force the compiler to wallow in detail. Compiler construction brings together ideas and techniques from across the breadth of computer science and applies them in a constrained setting to solve some truly hard problems.
The Fundamental Principles of Compilation
Compilers are large, complex, carefully engineered objects. While many issues in compiler design are amenable to multiple solutions and interpretations, there are two fundamental principles that a compiler writer must keep in mind at all times. The first principle is inviolable:
The compiler must preserve the meaning of the program being compiled.
Correctness is a fundamental issue in programming. The compiler must preserve correctness by faithfully implementing the "meaning" of its input program. This principle lies at the heart of the social contract between the compiler writer and compiler user. If the compiler can take liberties with meaning, then why not simply generate a nop or a return? If an incorrect translation is acceptable, why expend the effort to get it right?
The second principle that a compiler must observe is practical:
The compiler must improve the input program in some discernible way.
A traditional compiler improves the input program by making it directly executable on some target machine. Other "compilers" improve their input in different ways. For example, tpic is a program that takes the specification for a drawing written in the graphics language pic and converts it into LATEX; the "improvement" lies in LATEX's greater availability and generality. A source-to-source translator for c must produce code that is, in some measure, better than the input program; if it is not, why would anyone invoke it?
Excerpted from Engineering a Compiler by Keith D. Cooper Linda Torczon Copyright © 2012 by Elsevier, Inc.. Excerpted by permission of Morgan Kaufmann Publishers. All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
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Overview of Compilation; Scanning; Parsing; Context-Sensitive Analysis; Intermediate Representations; Procedure Abstraction; Code Shape; Introduction to Code Optimization; Data-Flow Analysis; Transformations; Instruction Selection; Instruction Scheduling; Register Allocation; Appendix A: ILOC; Appendix B: Data Structures; Appendix C: Abbreviations, Acronyms, and Glossary