Software transactional memory is a mechanism that provides programmers transactional semantic for accessing data in memory. In this section, you will learn how to apply these elements in Groovy. Take into account that we don't make an introduction to the Groovy programming language. You can find a lot of tutorials about the Groovy programming language on the internet. The main page about GPars is http://gpars.org. You can download the library and find documentation about how to use them. As we mentioned before, you also can use this library in the Java programming language.
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Mastering Concurrency Programming with Java 9, Second Edition
By :

Mastering Concurrency Programming with Java 9, Second Edition
By:
Overview of this book
Concurrency programming allows several large tasks to be divided into smaller sub-tasks, which are further processed as individual tasks that run in parallel. Java 9 includes a comprehensive API with lots of ready-to-use components for easily implementing powerful concurrency applications, but with high flexibility so you can adapt these components to your needs.
The book starts with a full description of the design principles of concurrent applications and explains how to parallelize a sequential algorithm. You will then be introduced to Threads and Runnables, which are an integral part of Java 9's concurrency API. You will see how to use all the components of the Java concurrency API, from the basics to the most advanced techniques, and will implement them in powerful real-world concurrency applications.
The book ends with a detailed description of the tools and techniques you can use to test a concurrent Java application, along with a brief insight into other concurrency mechanisms in JVM.
Table of Contents (14 chapters)
Preface
The First Step - Concurrency Design Principles
Working with Basic Elements - Threads and Runnables
Managing Lots of Threads - Executors
Getting the Most from Executors
Getting Data from Tasks - The Callable and Future Interfaces
Running Tasks Divided into Phases - The Phaser Class
Optimizing Divide and Conquer Solutions - The Fork/Join Framework
Processing Massive Datasets with Parallel Streams - The Map and Reduce Model
Processing Massive Datasets with Parallel Streams - The Map and Collect Model
Asynchronous Stream Processing - Reactive Streams
Diving into Concurrent Data Structures and Synchronization Utilities
Testing and Monitoring Concurrent Applications
Concurrency in JVM - Clojure and Groovy with the Gpars Library and Scala
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