Book Image

Mastering C++ Multithreading

By : Maya Posch
Book Image

Mastering C++ Multithreading

By: Maya Posch

Overview of this book

Multithreaded applications execute multiple threads in a single processor environment, allowing developers achieve concurrency. This book will teach you the finer points of multithreading and concurrency concepts and how to apply them efficiently in C++. Divided into three modules, we start with a brief introduction to the fundamentals of multithreading and concurrency concepts. We then take an in-depth look at how these concepts work at the hardware-level as well as how both operating systems and frameworks use these low-level functions. In the next module, you will learn about the native multithreading and concurrency support available in C++ since the 2011 revision, synchronization and communication between threads, debugging concurrent C++ applications, and the best programming practices in C++. In the final module, you will learn about atomic operations before moving on to apply concurrency to distributed and GPGPU-based processing. The comprehensive coverage of essential multithreading concepts means you will be able to efficiently apply multithreading concepts while coding in C++.
Table of Contents (17 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
8
Atomic Operations - Working with the Hardware

A basic OpenCL application


A common example of a GPGPU application is one which calculates the Fast Fourier Transform (FFT). This algorithm is commonly used for audio processing and similar, allowing you to transform, for example, from the time domain to the frequency domain for analysis purposes.

What it does is apply a divide and conquer approach to a dataset, in order to calculate the DFT (Discrete Fourier Transform). It does this by splitting the input sequence into a fixed, small number of smaller subsequences, computing their DFT, and assembling these outputs in order to compose the final sequence.

This is fairly advanced mathematics, but suffice it to say that what makes it so ideal for GPGPU is that it's a highly-parallel algorithm, employing the subdivision of data in order to speed up the calculating of the DFT, as visualized in this graphic:

Each OpenCL application consists of at least two parts: the C++ code that sets up and configures the OpenCL instance, and the actual OpenCL...