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  • Book Overview & Buying Learning .NET High-Performance Programming
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Learning .NET High-Performance Programming

Learning .NET High-Performance Programming

By : Antonio Esposito
3 (3)
Buy this Book
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Learning .NET High-Performance Programming

Learning .NET High-Performance Programming

3 (3)
By: Antonio Esposito
Buy this Book

Overview of this book

If you are a .NET developer with an understanding of application development, but want to learn how to optimize the performance of your applications, this is the book for you. Basic knowledge of C# is expected.
Table of Contents (11 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Performance Thoughts
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1. Performance Thoughts
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Understanding performance
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Performance as a requirement
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Performance engineering
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Performance aspects
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Class of applications
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Technical overview
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Summary
2
2. Architecting High-performance .NET Code
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2. Architecting High-performance .NET Code
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Software architecture
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Performance concerns about architecture
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Object-oriented design principles
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Common designs and architectures
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Architecture comparison
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Common platform architectures
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Performance considerations
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Summary
3
3. CLR Internals
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3. CLR Internals
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Introduction to CLR
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Memory management
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Garbage collection
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Working with AppDomains
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Threading
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Multithreading synchronization
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Exception handling
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Summary
4
4. Asynchronous Programming
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4. Asynchronous Programming
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Understanding asynchronous programming
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Asynchronous programming theory
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Asynchronous Programming Model (APM)
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Event-based Asynchronous Pattern (EAP)
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Task-based Asynchronous Pattern (TAP)
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Task UI synchronization
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Async/await
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Summary
5
5. Programming for Parallelism
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5. Programming for Parallelism
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Parallel programming
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Task parallelism with TPL
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Data parallelism with TPL
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Integrated querying with LINQ
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Data parallelism with PLINQ
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Summary
6
6. Programming for Math and Engineering
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6. Programming for Math and Engineering
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Introduction
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Evaluating the performance of data types
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Real-time applications
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Case study: Fourier transform
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Sliding processing
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Summary
7
7. Database Querying
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7. Database Querying
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Introduction
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Overview of ADO.NET
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An overview of Entity Framework
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Entity Framework querying optimization
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Entity Framework persistence optimization
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Performance comparison
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ADO.NET bulk copy
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Summary
8
8. Programming for Big Data
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8. Programming for Big Data
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What is big data?
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Architecting big data solutions
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Microsoft Azure for big data
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Simplified grid computing
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Lookup programming
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Summary
9
9. Analyzing Code Performance
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9. Analyzing Code Performance
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Software profiling
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Profiling with Visual Studio
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Testing with Visual Studio 2013
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Static program analysis
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Summary
10
Index
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Index

Parallel programming

The goal of any parallel programming is to reduce the whole latency time of the operation by using all the available local resources, in terms of CPU computational power.

Two definitions of parallelism actually exist. Task parallelism happens when we execute multiple jobs all together, such as saving data against multiple database servers.

Data parallelism, instead, happens when we split a huge dataset elaboration across all available CPUs, like when we have to execute some CPU demanding method against a huge amount of objects in the memory, like hashing data.

In the .NET framework, we have the ability to use both parallel kinds. Despite that, the most widely used kind of parallelism within the .NET framework's programming is data parallelism, thanks to PLINQ being so easy to use.

The following table shows the comparison between Task parallelism and Data parallelism:

 

Task parallelism

Data parallelism

What does it parallelize?

Parallelizable functions

Parallelizable...

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Tech Concepts
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Programming languages
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Learning .NET High-Performance Programming
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