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  • Book Overview & Buying LLVM Code Generation
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LLVM Code Generation

LLVM Code Generation

By : Quentin Colombet
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LLVM Code Generation

LLVM Code Generation

1 (1)
By: Quentin Colombet

Overview of this book

The LLVM infrastructure is a popular compiler ecosystem widely used in the tech industry and academia. This technology is crucial for both experienced and aspiring compiler developers looking to make an impact in the field. Written by Quentin Colombet, a veteran LLVM contributor and architect of the GlobalISel framework, this book provides a primer on the main aspects of LLVM, with an emphasis on its backend infrastructure; that is, everything needed to transform the intermediate representation (IR) produced by frontends like Clang into assembly code and object files. You’ll learn how to write an optimizing code generator for a toy backend in LLVM. The chapters will guide you step by step through building this backend while exploring key concepts, such as the ABI, cost model, and register allocation. You’ll also find out how to express these concepts using LLVM's existing infrastructure and how established backends address these challenges. Furthermore, the book features code snippets that demonstrate the actual APIs. By the end of this book, you’ll have gained a deeper understanding of LLVM. The concepts presented are expected to remain stable across different LLVM versions, making this book a reliable quick reference guide for understanding LLVM.
Table of Contents (30 chapters)
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Part 1: Getting Started with LLVM
8
Part 2: Middle-End: LLVM IR to LLVM IR
13
Part 3: Introduction to the Backend
17
Part 4: LLVM IR to Machine IR
22
Part 5: Final Lowering and Optimizations
28
Other Books You May Enjoy
29
Index

Using the right abstraction

As we close our journey with LLVM IR, and before we start digging into Machine IR, we wanted to give you a few recommendations on how to decide if you need to implement your optimization passes in LLVM IR or in Machine IR.

Before you decide whether you should implement something in one IR or the other, step back and think about what you are trying to achieve.

Transformations at the LLVM IR level are easier to reuse across backends and are usually easier to write. Conversely, Machine IR passes have to provide additional mechanisms, such as helper functions and classes, to abstract away target-specific constructs. For instance, things as simple as recognizing an add instruction are not portable across targets in Machine IR, unless you push some logic on the target itself.

In contrast, LLVM IR passes are at the mercy of what later transformations will do, including the Machine IR passes. For instance, computing an estimate of the register pressure...

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LLVM Code Generation
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