Book Image

Multithreading with C# Cookbook, Second Edition - Second Edition

By : Evgenii Agafonov
Book Image

Multithreading with C# Cookbook, Second Edition - Second Edition

By: Evgenii Agafonov

Overview of this book

Multi-core processors are synonymous with computing speed and power in today’s world, which is why multithreading has become a key concern for C# developers. Multithreaded code helps you create effective, scalable, and responsive applications. This is an easy-to-follow guide that will show you difficult programming problems in context. You will learn how to solve them with practical, hands-on, recipes. With these recipes, you’ll be able to start creating your own scalable and reliable multithreaded applications. Starting from learning what a thread is, we guide you through the basics and then move on to more advanced concepts such as task parallel libraries, C# asynchronous functions, and much more. Rewritten to the latest C# specification, C# 6, and updated with new and modern recipes to help you make the most of the hardware you have available, this book will help you push the boundaries of what you thought possible in C#.
Table of Contents (18 chapters)
Multithreading with C# Cookbook Second Edition
About the Author
About the Reviewers


In .NET Framework, there is a subset of libraries that is called Parallel Framework, often referred to as Parallel Framework Extensions (PFX), which was the name of the very first version of these libraries. Parallel Framework was released with .NET Framework 4.0 and consists of three major parts:

  • The Task Parallel Library (TPL)

  • Concurrent collections

  • Parallel LINQ or PLINQ

Until now, you have learned how to run several tasks in parallel and synchronize them with one another. In fact, we partitioned our program into a set of tasks and had different threads running different tasks. This approach is called task parallelism, and you have only been learning about task parallelism so far.

Imagine that we have a program that performs some heavy calculations over a big set of data. The easiest way to parallelize this program is to partition this set of data into smaller chunks, run the calculations needed over these chunks of data in parallel, and then aggregate the results of these calculations...