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

You're reading from   Hands-On Parallel Programming with C# 8 and .NET Core 3 Build solid enterprise software using task parallelism and multithreading

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Product type Paperback
Published in Dec 2019
Publisher Packt
ISBN-13 9781789132410
Length 346 pages
Edition 1st Edition
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Author (1):
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Shakti Tanwar Shakti Tanwar
Author Profile Icon Shakti Tanwar
Shakti Tanwar
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Table of Contents (22) Chapters Close

Preface 1. Section 1: Fundamentals of Threading, Multitasking, and Asynchrony FREE CHAPTER
2. Introduction to Parallel Programming 3. Task Parallelism 4. Implementing Data Parallelism 5. Using PLINQ 6. Section 2: Data Structures that Support Parallelism in .NET Core
7. Synchronization Primitives 8. Using Concurrent Collections 9. Improving Performance with Lazy Initialization 10. Section 3: Asynchronous Programming Using C#
11. Introduction to Asynchronous Programming 12. Async, Await, and Task-Based Asynchronous Programming Basics 13. Section 4: Debugging, Diagnostics, and Unit Testing for Async Code
14. Debugging Tasks Using Visual Studio 15. Writing Unit Test Cases for Parallel and Asynchronous Code 16. Section 5: Parallel Programming Feature Additions to .NET Core
17. IIS and Kestrel in ASP.NET Core 18. Patterns in Parallel Programming 19. Distributed Memory Management 20. Assessments 21. Other Books You May Enjoy

Introduction to distributed systems

We have already discussed how distributed computing works in this book. In this section, we will try to understand distributed computing with a small example that works on arrays.

Let's say we have an array of 1,040 elements and we would like to find the sum of all the numbers:

a = [1,2,3, 4...., n]

If the total time that's taken to add numbers is x (let's say all of the numbers are large) and we want to compute them all as fast as possible, we can take advantage of distributed computing. We would divide the array into multiple arrays (let's say, four arrays), each with 25% of the original number of elements, and send each array to a different processor to calculate the sum, as follows:

In this arrangement, the total time that's taken to add all the numbers is reduced to (x/4 + d) or (x/number of processors +d), where...

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