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Hands-On High Performance Programming with Qt 5

Hands-On High Performance Programming with Qt 5

By : Marek Krajewski
5 (2)
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Hands-On High Performance Programming with Qt 5

Hands-On High Performance Programming with Qt 5

5 (2)
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)
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Profiling CPU usage

On Linux Qt, Creator integrates with Valgrind's callgrind, which, alas, isn't available on Windows, so we have to look for other tools. Before we jump to the dedicated tools, let's discuss a very basic, little-known (as far as I can judge), but surprisingly effective, technique.

Poor man's sampling technique

I call that technique the poor man's sampling technique. It is really simple. In its most basic form, you run a program in a debugger, stop it several times, and examine the call stack of the randomly chosen breaks. The idea is (as with sampling profilers) that the most-used function will show most often in the stack trace. Simple as it is, this quick technique will sometimes...

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