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)
Section 1: The Basics
Writing Your First Kernel Module - LKMs Part 2
Section 2: Understanding and Working with the Kernel
Kernel Memory Allocation for Module Authors - Part 1
Kernel Memory Allocation for Module Authors - Part 2
Section 3: Delving Deeper
About Packt

Data structures – a few design tips

Using the slab APIs for memory allocation in kernel space is highly recommended. For one, it guarantees both physically contiguous as well as cacheline-aligned memory. This is very good for performance; in addition, let's check out a few quick tips that can yield big returns.

CPU caching can provide tremendous performance gains. Thus, especially for time-critical code, take care to design your data structures for best performance:

  • Keep the most important (frequently accessed, "hot") members  together and at the top of the structure. To see why, imagine there are five important members (of a total size of say, 56 bytes) in your data structure; keep them all together and at the top of the structure. Say the CPU cacheline size is 64 bytes. Now, when your code accesses any one of these five important members (for anything, read/write), all five members will be fetched into the...