Book Image

High-Performance Programming in C# and .NET

By : Jason Alls
Book Image

High-Performance Programming in C# and .NET

By: Jason Alls

Overview of this book

Writing high-performance code while building an application is crucial, and over the years, Microsoft has focused on delivering various performance-related improvements within the .NET ecosystem. This book will help you understand the aspects involved in designing responsive, resilient, and high-performance applications with the new version of C# and .NET. You will start by understanding the foundation of high-performance code and the latest performance-related improvements in C# 10.0 and .NET 6. Next, you’ll learn how to use tracing and diagnostics to track down performance issues and the cause of memory leaks. The chapters that follow then show you how to enhance the performance of your networked applications and various ways to improve directory tasks, file tasks, and more. Later, you’ll go on to improve data querying performance and write responsive user interfaces. You’ll also discover how you can use cloud providers such as Microsoft Azure to build scalable distributed solutions. Finally, you’ll explore various ways to process code synchronously, asynchronously, and in parallel to reduce the time it takes to process a series of tasks. By the end of this C# programming book, you’ll have the confidence you need to build highly resilient, high-performance applications that meet your customer's demands.
Table of Contents (22 chapters)
1
Part 1: High-Performance Code Foundation
7
Part 2: Writing High-Performance Code
16
Part 3: Threading and Concurrency

Summary

In this chapter, we looked at how to use TPL and PLINQ to execute code in parallel. At this point, we understand that the main difference between TPL and PLINQ is that TPL does not efficiently utilize all the cores on a computer, whereas PLINQ does.

We also saw how we can view the computer’s CPU utilization. Using PLINQ enables us to utilize all the cores of a CPU efficiently to improve code performance. However, when benchmarking parallel code, we saw that it is sometimes faster than non-parallel code, while other times, it is faster. Therefore, it pays to benchmark your code to see what method works best for you.

We also reviewed a piece of code that demonstrates the use of lambda expressions for expressing both Func and Action delegates.

Finally, we looked at debugging parallel applications with a code sample that employed the Parallel Tasks window, the Tasks pane, and the Concurrency Visualizer.

In the next chapter, we will look at asynchronous programming...