Book Image

Linux Kernel Programming

By : Kaiwan N. Billimoria
Book Image

Linux Kernel Programming

By: Kaiwan N. Billimoria

Overview of this book

Linux Kernel Programming is a comprehensive introduction for those new to Linux kernel and module development. This easy-to-follow guide will have you up and running with writing kernel code in next-to-no time. This book uses the latest 5.4 Long-Term Support (LTS) Linux kernel, which will be maintained from November 2019 through to December 2025. By working with the 5.4 LTS kernel throughout the book, you can be confident that your knowledge will continue to be valid for years to come. You’ll start the journey by learning how to build the kernel from the source. Next, you’ll write your first kernel module using the powerful Loadable Kernel Module (LKM) framework. The following chapters will cover key kernel internals topics including Linux kernel architecture, memory management, and CPU scheduling. During the course of this book, you’ll delve into the fairly complex topic of concurrency within the kernel, understand the issues it can cause, and learn how they can be addressed with various locking technologies (mutexes, spinlocks, atomic, and refcount operators). You’ll also benefit from more advanced material on cache effects, a primer on lock-free techniques within the kernel, deadlock avoidance (with lockdep), and kernel lock debugging techniques. By the end of this kernel book, you’ll have a detailed understanding of the fundamentals of writing Linux kernel module code for real-world projects and products.
Table of Contents (19 chapters)
1
Section 1: The Basics
6
Writing Your First Kernel Module - LKMs Part 2
7
Section 2: Understanding and Working with the Kernel
10
Kernel Memory Allocation for Module Authors - Part 1
11
Kernel Memory Allocation for Module Authors - Part 2
14
Section 3: Delving Deeper
17
About Packt

Step 1 – cloning the kernel source tree

We arbitrarily select a staging folder (the place where the build happens) for the kernel source tree and the cross-toolchain, and assign it to an environment variable (so as to avoid hard-coding it):

  1. Set up your workspace. We set an environment variable as RPI_STG (it's not required to use exactly this name for the environment variable; just pick a reasonable-sounding name and stick to it) to the staging folder's location – the place where we shall perform the work. Feel free to use a value appropriate to your system:
export RPI_STG=~/rpi_work
mkdir -p ${RPI_STG}/kernel_rpi ${RPI_STG}/rpi_tools
Do ensure you have sufficient disk space available: the kernel source tree takes approximately 900 MB, and the toolchain around 1.5 GB. You'll require at least another gigabyte for working space.
  1. Download the Raspberry Pi kernel source tree (we clone it from the official source, the Raspberry Pi GitHub repository for the...