Book Image

Mastering Embedded Linux Programming - Third Edition

By : Frank Vasquez, Chris Simmonds
5 (3)
Book Image

Mastering Embedded Linux Programming - Third Edition

5 (3)
By: Frank Vasquez, Chris Simmonds

Overview of this book

If you’re looking for a book that will demystify embedded Linux, then you’ve come to the right place. Mastering Embedded Linux Programming is a fully comprehensive guide that can serve both as means to learn new things or as a handy reference. The first few chapters of this book will break down the fundamental elements that underpin all embedded Linux projects: the toolchain, the bootloader, the kernel, and the root filesystem. After that, you will learn how to create each of these elements from scratch and automate the process using Buildroot and the Yocto Project. As you progress, the book will show you how to implement an effective storage strategy for flash memory chips and install updates to a device remotely once it’s deployed. You’ll also learn about the key aspects of writing code for embedded Linux, such as how to access hardware from apps, the implications of writing multi-threaded code, and techniques to manage memory in an efficient way. The final chapters demonstrate how to debug your code, whether it resides in apps or in the Linux kernel itself. You’ll also cover the different tracers and profilers that are available for Linux so that you can quickly pinpoint any performance bottlenecks in your system. By the end of this Linux book, you’ll be able to create efficient and secure embedded devices using Linux.
Table of Contents (27 chapters)
1
Section 1: Elements of Embedded Linux
10
Section 2: System Architecture and Design Decisions
18
Section 3: Writing Embedded Applications
22
Section 4: Debugging and Optimizing Performance

Tracing events

The tools we have seen so far all use statistical sampling. You often want to know more about the ordering of events so that you can see them and relate them to each other. Function tracing involves instrumenting the code with tracepoints that capture information about the event, and may include some or all of the following:

  • A timestamp
  • Context, such as the current PID
  • Function parameters and return values
  • A callstack

It is more intrusive than statistical profiling and it can generate a large amount of data. The latter problem can be mitigated by applying filters when the sample is captured and later on when viewing the trace.

I will cover three trace tools here: the kernel function tracers Ftrace, LTTng, and BPF.