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2.2 Futures in Java's Fork/Join Framework - Parallel Programming in Java
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2.2 Futures in Java's Fork/Join Framework - Parallel Programming in Java
Parallel, Concurrent, and Distributed Programming in Java Specialization
This course teaches learners (industry professionals and students) the fundamental concepts of parallel programming in the context of Java 8. Parallel programming enables developers to use multicore computers to make their applications run faster by using multiple processors at the same time. By the end of this course, you will learn how to use popular parallel Java frameworks (such as ForkJoin, Stream, and Phaser) to write parallel programs for a wide range of multicore platforms including servers, desktops, or mobile devices, while also learning about their theoretical foundations including computation graphs, ideal parallelism, parallel speedup, Amdahl's Law, data races, and determinism.
Why take this course?
• All computers are multicore computers, so it is important for you to learn how to extend your knowledge of sequential Java programming to multicore parallelism.
• Java 7 and Java 8 have introduced new frameworks for parallelism (ForkJoin, Stream) that have significantly changed the paradigms for parallel programming since the early days of Java.
• Each of the four modules in the course includes an assigned mini-project that will provide you with the necessary hands-on experience to use the concepts learned in the course on your own, after the course ends.
• During the course, you will have online access to the instructor and the mentors to get individualized answers to your questions posted on forums.
The desired learning outcomes of this course are as follows:
• Theory of parallelism: computation graphs, work, span, ideal parallelism, parallel speedup, Amdahl's Law, data races, and determinism
• Task parallelism using Java’s ForkJoin framework
• Functional parallelism using Java’s Future and Stream frameworks
• Loop-level parallelism with extensions for barriers and iteration grouping (chunking)
• Dataflow parallelism using the Phaser framework and data-driven tasks
Mastery of these concepts will enable you to immediately apply them in the context of multicore Java programs, and will also provide the foundation for mastering other parallel programming systems that you may encounter in the future (e.g., C++11, OpenMP, .Net Task Parallel Library).
Dataflow, Parallel Computing, Java Concurrency, Data Parallelism
I have never known these things existed and changed my perspective how to take advantage of multiple processor. I have used numpy in python but now I know how these are fast.,I like the course very much. I am a working software engineer and I believe it will be helpful in my work. Prof Vivek is so clear in explanation and pretty to the point.
Welcome to Module 2! In this module, we will learn about approaches to parallelism that have been inspired by functional programming. Advocates of parallel functional programming have argued for decades that functional parallelism can eliminate many hard-to-detect bugs that can occur with imperative parallelism. We will learn about futures, memoization, and streams, as well as data races, a notorious class of bugs that can be avoided with functional parallelism. We will also learn Java APIs for functional parallelism, including the Fork/Join framework and the Stream API’s.
2.2 Futures in Java's Fork/Join Framework - Parallel Programming in Java
Copyright Disclaimer under Section 107 of the copyright act 1976, allowance is made for fair use for purposes such as criticism, comment, news reporting, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favour of fair use.
2.2 Futures in Java's Fork/Join Framework - Parallel Programming in Java
Parallel, Concurrent, and Distributed Programming in Java Specialization
This course teaches learners (industry professionals and students) the fundamental concepts of parallel programming in the context of Java 8. Parallel programming enables developers to use multicore computers to make their applications run faster by using multiple processors at the same time. By the end of this course, you will learn how to use popular parallel Java frameworks (such as ForkJoin, Stream, and Phaser) to write parallel programs for a wide range of multicore platforms including servers, desktops, or mobile devices, while also learning about their theoretical foundations including computation graphs, ideal parallelism, parallel speedup, Amdahl's Law, data races, and determinism.
Why take this course?
• All computers are multicore computers, so it is important for you to learn how to extend your knowledge of sequential Java programming to multicore parallelism.
• Java 7 and Java 8 have introduced new frameworks for parallelism (ForkJoin, Stream) that have significantly changed the paradigms for parallel programming since the early days of Java.
• Each of the four modules in the course includes an assigned mini-project that will provide you with the necessary hands-on experience to use the concepts learned in the course on your own, after the course ends.
• During the course, you will have online access to the instructor and the mentors to get individualized answers to your questions posted on forums.
The desired learning outcomes of this course are as follows:
• Theory of parallelism: computation graphs, work, span, ideal parallelism, parallel speedup, Amdahl's Law, data races, and determinism
• Task parallelism using Java’s ForkJoin framework
• Functional parallelism using Java’s Future and Stream frameworks
• Loop-level parallelism with extensions for barriers and iteration grouping (chunking)
• Dataflow parallelism using the Phaser framework and data-driven tasks
Mastery of these concepts will enable you to immediately apply them in the context of multicore Java programs, and will also provide the foundation for mastering other parallel programming systems that you may encounter in the future (e.g., C++11, OpenMP, .Net Task Parallel Library).
Dataflow, Parallel Computing, Java Concurrency, Data Parallelism
I have never known these things existed and changed my perspective how to take advantage of multiple processor. I have used numpy in python but now I know how these are fast.,I like the course very much. I am a working software engineer and I believe it will be helpful in my work. Prof Vivek is so clear in explanation and pretty to the point.
Welcome to Module 2! In this module, we will learn about approaches to parallelism that have been inspired by functional programming. Advocates of parallel functional programming have argued for decades that functional parallelism can eliminate many hard-to-detect bugs that can occur with imperative parallelism. We will learn about futures, memoization, and streams, as well as data races, a notorious class of bugs that can be avoided with functional parallelism. We will also learn Java APIs for functional parallelism, including the Fork/Join framework and the Stream API’s.
2.2 Futures in Java's Fork/Join Framework - Parallel Programming in Java
Copyright Disclaimer under Section 107 of the copyright act 1976, allowance is made for fair use for purposes such as criticism, comment, news reporting, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favour of fair use.
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