Book Image

Hands-On High Performance Programming with Qt 5

By : Marek Krajewski
5 (1)
Book Image

Hands-On High Performance Programming with Qt 5

5 (1)
By: Marek Krajewski

Overview of this book

Achieving efficient code through performance tuning is one of the key challenges faced by many programmers. This book looks at Qt programming from a performance perspective. You'll explore the performance problems encountered when using the Qt framework and means and ways to resolve them and optimize performance. The book highlights performance improvements and new features released in Qt 5.9, Qt 5.11, and 5.12 (LTE). You'll master general computer performance best practices and tools, which can help you identify the reasons behind low performance, and the most common performance pitfalls experienced when using the Qt framework. In the following chapters, you’ll explore multithreading and asynchronous programming with C++ and Qt and learn the importance and efficient use of data structures. You'll also get the opportunity to work through techniques such as memory management and design guidelines, which are essential to improve application performance. Comprehensive sections that cover all these concepts will prepare you for gaining hands-on experience of some of Qt's most exciting application fields - the mobile and embedded development domains. By the end of this book, you'll be ready to build Qt applications that are more efficient, concurrent, and performance-oriented in nature
Table of Contents (14 chapters)

Algorithms, data structures, and performance

This is an intermediate-level book, so you will probably know a lot about algorithms and data structures already, but we will nonetheless first provide some high-level overview for these topics before we discuss the Qt-specific use cases and gotchas. Let's start with algorithms.

Algorithm classes

Algorithms are recipes for calculating something. As you are an intermediate programmer, you might surely have heard about the O(n) (that is, the big O) notation which ranks the algorithms performance-wise. This notation describes the relative theoretical performance of algorithms by measuring how the runtime of an algorithm increases with increasing input size. The variable n stands...