Book Image

Hands-On Parallel Programming with C# 8 and .NET Core 3

By : Shakti Tanwar
Book Image

Hands-On Parallel Programming with C# 8 and .NET Core 3

By: Shakti Tanwar

Overview of this book

In today’s world, every CPU has a multi-core processor. However, unless your application has implemented parallel programming, it will fail to utilize the hardware’s full processing capacity. This book will show you how to write modern software on the optimized and high-performing .NET Core 3 framework using C# 8. Hands-On Parallel Programming with C# 8 and .NET Core 3 covers how to build multithreaded, concurrent, and optimized applications that harness the power of multi-core processors. Once you’ve understood the fundamentals of threading and concurrency, you’ll gain insights into the data structure in .NET Core that supports parallelism. The book will then help you perform asynchronous programming in C# and diagnose and debug parallel code effectively. You’ll also get to grips with the new Kestrel server and understand the difference between the IIS and Kestrel operating models. Finally, you’ll learn best practices such as test-driven development, and run unit tests on your parallel code. By the end of the book, you’ll have developed a deep understanding of the core concepts of concurrency and asynchrony to create responsive applications that are not CPU-intensive.
Table of Contents (22 chapters)
Free Chapter
Section 1: Fundamentals of Threading, Multitasking, and Asynchrony
Section 2: Data Structures that Support Parallelism in .NET Core
Section 3: Asynchronous Programming Using C#
Section 4: Debugging, Diagnostics, and Unit Testing for Async Code
Section 5: Parallel Programming Feature Additions to .NET Core

The MapReduce pattern

The MapReduce pattern was introduced in order to handle big data problems such as large-scale computing requirements spanning across a cluster of servers. The pattern can also be used on single-core machines.

A MapReduce program is composed of two tasks: map and reduce. The input for the MapReduce program is passed as a set of key-value pairs and the output is also received as such.

To implement this pattern, we need to start by writing a map function that takes in data (key/value pair) as a single input value and converts it into another set of intermediate data (key/value pair). The user then writes a reduce function that takes the output from the map function (key/value pair) as input and combines the data with a smaller dataset containing any number of rows of data.

Let's look at how to implement a basic MapReduce pattern using LINQ and convert it...