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Mastering Concurrency Programming with Java 9, Second Edition

You're reading from   Mastering Concurrency Programming with Java 9, Second Edition Fast, reactive and parallel application development

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781785887949
Length 516 pages
Edition 2nd Edition
Languages
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Author (1):
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Javier Fernández González Javier Fernández González
Author Profile Icon Javier Fernández González
Javier Fernández González
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Table of Contents (14) Chapters Close

Preface 1. The First Step - Concurrency Design Principles FREE CHAPTER 2. Working with Basic Elements - Threads and Runnables 3. Managing Lots of Threads - Executors 4. Getting the Most from Executors 5. Getting Data from Tasks - The Callable and Future Interfaces 6. Running Tasks Divided into Phases - The Phaser Class 7. Optimizing Divide and Conquer Solutions - The Fork/Join Framework 8. Processing Massive Datasets with Parallel Streams - The Map and Reduce Model 9. Processing Massive Datasets with Parallel Streams - The Map and Collect Model 10. Asynchronous Stream Processing - Reactive Streams 11. Diving into Concurrent Data Structures and Synchronization Utilities 12. Testing and Monitoring Concurrent Applications 13. Concurrency in JVM - Clojure and Groovy with the Gpars Library and Scala

Optimizing Divide and Conquer Solutions - The Fork/Join Framework

In Chapter 3, Managing Lots of Threads - Executors, Chapter 4, Getting the Most from Executors, and Chapter 5, Getting Data from Tasks - The Callable and Future Interfaces, you learned how to work with executors as a mechanism to improve the performance of concurrent applications that execute lots of concurrent tasks. The Java 7 Concurrency API introduced a special kind of executor through the fork/join framework. This framework is designed to implement optimal concurrent solutions to those problems that can be solved using the divide and conquer design paradigm. In this chapter, we will cover the following topics:

  • An introduction to the fork/join framework
  • The first example - the k-means clustering algorithm
  • The second example - a data filtering algorithm
  • The third example - the merge sort algorithm
...
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