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  • Book Overview & Buying Mastering C# Concurrency
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Mastering C# Concurrency

Mastering C# Concurrency

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Mastering C# Concurrency

Mastering C# Concurrency

2.3 (3)
Buy this Book

Overview of this book

Starting with the traditional approach to concurrency, you will learn how to write multithreaded concurrent programs and compose ways that won't require locking. You will explore the concepts of parallelism granularity, and fine-grained and coarse-grained parallel tasks by choosing a concurrent program structure and parallelizing the workload optimally. You will also learn how to use task parallel library, cancellations, timeouts, and how to handle errors. You will know how to choose the appropriate data structure for a specific parallel algorithm to achieve scalability and performance. Further, you'll learn about server scalability, asynchronous I/O, and thread pools, and write responsive traditional Windows and Windows Store applications. By the end of the book, you will be able to diagnose and resolve typical problems that could happen in multithreaded applications.
Table of Contents (12 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
Lock Free Chapter
1
1. Traditional Concurrency
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1. Traditional Concurrency
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What's the problem?
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Using locks
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Reader-writer lock
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Spin lock
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Optimization strategy
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Summary
2
2. Lock-Free Concurrency
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2. Lock-Free Concurrency
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Memory model and compiler optimizations
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The System.Threading.Interlocked class
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Interlocked internals
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Writing lock-free code
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Summary
3
3. Understanding Parallelism Granularity
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3. Understanding Parallelism Granularity
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The number of threads
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Using the thread pool
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Understanding granularity
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Choosing the coarse-grained or fine-grained approach
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Summary
4
4. Task Parallel Library in Depth
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4. Task Parallel Library in Depth
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Task composition
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Tasks hierarchy
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Awaiting task completion
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Task cancellation
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Latency and the coarse-grained approach with TPL
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Exception handling
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Using the Parallel class
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Summary
5
5. C# Language Support for Asynchrony
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5. C# Language Support for Asynchrony
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Implementing the downloading of images from Bing
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Is the async keyword really needed?
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Fire-and-forget tasks
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Other useful TPL features
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Implementing a custom awaitable type
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Summary
6
6. Using Concurrent Data Structures
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6. Using Concurrent Data Structures
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Standard collections and synchronization primitives
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Implementing a cache with ReaderWriterLockSlim
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Concurrent collections in .NET
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ConcurrentDictionary
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ConcurrentBag
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ConcurrentQueue
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ConcurrentStack
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The Producer/Consumer pattern
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The Producer/Consumer pattern in .NET 4.0+
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Summary
7
7. Leveraging Parallel Patterns
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7. Leveraging Parallel Patterns
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Concurrent idioms
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Asynchronous patterns
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Concurrent patterns
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Summary
8
8. Server-side Asynchrony
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8. Server-side Asynchrony
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Server applications
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The OWIN Web API framework
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Load testing and scalability
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I/O and CPU-bound tasks
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Deep dive into asynchronous I/O
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Real and fake asynchronous I/O operations
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Synchronization context
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CPU-bound tasks and queues
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Summary
9
9. Concurrency in the User Interface
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9. Concurrency in the User Interface
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The importance of asynchrony for UI
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UI threads and message loops
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Common problems and solutions
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How the await keyword works
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Performance issues
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Summary
10
10. Troubleshooting Parallel Programs
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10. Troubleshooting Parallel Programs
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How troubleshooting parallel programs is different
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Writing tests
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Integration tests
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Debugging
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Performance measurement and profiling
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Summary
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Index
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Index

Chapter 3. Understanding Parallelism Granularity

One of the most essential tasks when writing parallel code is to divide your program into subsets that will run in parallel and communicate between each other. Sometimes the task naturally divides into separate pieces, but usually it is up to you to choose which parts to make parallel. Should we use a small number of large tasks, many small tasks, or maybe large and small tasks at the same time?

Theoretically speaking, it does not matter. In case of an ideal computational device, it would have no overhead for creating a worker thread and distributing work between any numbers of threads. However, on a real CPU, this performance overhead is significant and it is very important to take this into account. The right way to split your program into parallel parts is the key to writing effective and fast programs. In this chapter, we are going to review this problem in detail.

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