Book Image

Learn LLVM 12

By : Kai Nacke
Book Image

Learn LLVM 12

By: Kai Nacke

Overview of this book

LLVM was built to bridge the gap between compiler textbooks and actual compiler development. It provides a modular codebase and advanced tools which help developers to build compilers easily. This book provides a practical introduction to LLVM, gradually helping you navigate through complex scenarios with ease when it comes to building and working with compilers. You’ll start by configuring, building, and installing LLVM libraries, tools, and external projects. Next, the book will introduce you to LLVM design and how it works in practice during each LLVM compiler stage: frontend, optimizer, and backend. Using a subset of a real programming language as an example, you will then learn how to develop a frontend and generate LLVM IR, hand it over to the optimization pipeline, and generate machine code from it. Later chapters will show you how to extend LLVM with a new pass and how instruction selection in LLVM works. You’ll also focus on Just-in-Time compilation issues and the current state of JIT-compilation support that LLVM provides, before finally going on to understand how to develop a new backend for LLVM. By the end of this LLVM book, you will have gained real-world experience in working with the LLVM compiler development framework with the help of hands-on examples and source code snippets.
Table of Contents (17 chapters)
1
Section 1 – The Basics of Compiler Construction with LLVM
5
Section 2 – From Source to Machine Code Generation
11
Section 3 –Taking LLVM to the Next Level

Utilizing a JIT compiler for code evaluation

Compiler writers make a great effort to produce optimal code. A simple, yet effective, optimization is to replace an arithmetic operation on two constants by the result value of this operation. To be able to perform the computation, an interpreter for constant expressions is embedded. And to arrive at the same result, the interpreter has to implement the same rules as the generated machine code! Of course, this can be the source of subtle errors.

A different approach would be to compile the constant expression to IR using the same code generations methods, and then have JIT compile and execute the IR. This idea can even be taken a step further. In mathematics, a function always produces the same result for the same input. For functions in computer languages, this is not true. A good example is the rand() function, which returns a random value for each call. A function in computer languages, which has the same characteristic as a function...