Compilers: principles, techniques, and tools

2011

As our group programmed a calculator throughout this semester, we learned a lot about the specifics about the HP 20b business calculator, while also discovered the basics of programming in C. The class was designed to expose us, many for the first time, to the world of computer science. To begin with, we looked into how to make the display work by creating a message that scrolls across the screen. This introduced us to writing code while beginning to make us familiar with the calculator. The next lab had us explore keyboards. After actually taking different keyboards apart to explore their design, we programmed how to know if a button on the actual calculator is pushed. Then we made the calculator begin to operate which involved reading the numbers pressed and displaying them on the screen while exploring deeper complexities of writing code. Finally we began to put everything together to make the calculator actually function with basic operations. We set it up as an Reverse Polish N...

Preface Vision Compiler construction brings together techniques from disparate parts of Computer Science. The compiler deals with many big-picture issues. At its simplest, a compiler is just a computer program that takes as input one potentially exe-cutable program and produces as output another, related, potentially executable program. As part of this translation, the compiler must perform syntax analysis to determine if the input program is valid. To map that input program onto the finite resources of a target computer, the compiler must manipulate several distinct name spaces, allocate several different kinds of resources, and synchronize the behavior of different run-time components. For the output program to have reasonable performance, it must manage hardware latencies in functional units, predict the flow of execution and the demand for memory, and reason about the independence and dependence of different machine-level operations in the program. Open up a compiler and you are likely to find greedy heuristic searches that explore large solution spaces, finite automata that recognize words in the input, fixed-point algorithms that help reason about program behavior, simple theorem provers and algebraic simplifiers that try to predict the values of expressions, pattern-matchers for both strings and trees that match abstract computations to machine-level operations, solvers for diophantine equations and Pressburger arithmetic used to analyze array subscripts, and techniques such as hash tables, graph algorithms, and sparse set implementations used in myriad applications, The lore of compiler construction includes both amazing success stories about the application of theory to practice and humbling stories about the limits of what we can do. On the success side, modern scanners are built by applying the theory of regular languages to automatic construction of recognizers. Lr parsers use the same techniques to perform the handle-recognition that drives a shift-reduce parser. Data-flow analysis (and its cousins) apply lattice theory to the analysis of programs in ways that are both useful and clever. Some of the problems that a compiler faces are truly hard; many clever approximations and heuristics have been developed to attack these problems. On the other side, we have discovered that some of the problems that compilers must solve are quite hard. For example, the back end of a compiler for a modern superscalar machine must approximate the solution to two or more iii