本课程是 Parallel, Concurrent, and Distributed Programming in Java 专项课程专项课程的一部分

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Vivek Sarkar

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Parallel, Concurrent, and Distributed Programming in Java 专项课程莱斯大学

课程信息

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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).

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第 1 门课程（共 3 门）

Parallel, Concurrent, and Distributed Programming in Java 专项课程

中级

完成时间大约为19 小时

英语（English）

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您将获得的技能

Dataflow
Parallel Computing
Java Concurrency
Data Parallelism

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可分享的证书

完成后获得证书

100% 在线

立即开始，按照自己的计划学习。

第 1 门课程（共 3 门）

Parallel, Concurrent, and Distributed Programming in Java 专项课程

中级

完成时间大约为19 小时

英语（English）

对员工进行热门技能培训能否为您的公司带来益处？

体验 Coursera 企业版

授课教师

授课教师评分

4.82/5 （111 个评分）

Vivek Sarkar

Professor

Department of Computer Science

52,164 个学生

3 门课程

提供方

莱斯大学

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内容评分95%（4,259 个评分）

周

周 1

完成时间为 1 小时

Welcome to the Course!

完成时间为 1 小时

1 个视频（总计 1 分钟）, 5 个阅读材料, 1 个测验

完成时间为 4 小时

Task Parallelism

In this module, we will learn the fundamentals of task parallelism. Tasks are the most basic unit of parallel programming. An increasing number of programming languages (including Java and C++) are moving from older thread-based approaches to more modern task-based approaches for parallel programming. We will learn about task creation, task termination, and the “computation graph” theoretical model for understanding various properties of task-parallel programs. These properties include work, span, ideal parallelism, parallel speedup, and Amdahl’s Law. We will also learn popular Java APIs for task parallelism, most notably the Fork/Join framework.

完成时间为 4 小时

7 个视频（总计 42 分钟）, 6 个阅读材料, 2 个测验

7 个视频

1.1 Task Creation and Termination (Async, Finish)4分钟

1.2 Tasks in Java's Fork/Join Framework5分钟

1.3 Computation Graphs, Work, Span7分钟

1.4 Multiprocessor Scheduling, Parallel Speedup8分钟

1.5 Amdahl's Law5分钟

ReciprocalArraySum using Async-Finish (Demo)4分钟

ReciprocalArraySum using RecursiveAction's in Java's Fork/Join Framework (Demo)5分钟

6 个阅读材料

1.1 Lecture Summary5分钟

1.2 Lecture Summary5分钟

1.3 Lecture Summary5分钟

1.4 Lecture Summary5分钟

1.5 Lecture Summary5分钟

Mini Project 1: Reciprocal-Array-Sum using the Java Fork/Join Framework10分钟

1 个练习

Module 1 Quiz30分钟

周

周 2

完成时间为 4 小时

Functional Parallelism

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.

完成时间为 4 小时

7 个视频（总计 40 分钟）, 6 个阅读材料, 2 个测验

7 个视频

2.1 Futures: Tasks with Return Values6分钟

2.2 Futures in Java's Fork/Join Framework5分钟

2.3 Memoization6分钟

2.4 Java Streams5分钟

2.5 Data Races and Determinism9分钟

ReciprocalArraySum using RecursiveTask's in Java's Fork/Join Framework (Demo)3分钟

Parallel List Processing Using Java Streams (Demo)4分钟

6 个阅读材料

2.1 Lecture Summary10分钟

2.2 Lecture Summary10分钟

2.3 Lecture Summary10分钟

2.4 Lecture Summary10分钟

2.5 Lecture Summary10分钟

Mini Project 2: Analyzing Student Statistics Using Java Parallel Streams10分钟

1 个练习

Module 2 Quiz30分钟

完成时间为 23 分钟

Talking to Two Sigma: Using it in the Field

完成时间为 23 分钟

2 个视频（总计 13 分钟）, 1 个阅读材料

周

周 3

完成时间为 4 小时

Loop Parallelism

Welcome to Module 3, and congratulations on reaching the midpoint of this course! It is well known that many applications spend a majority of their execution time in loops, so there is a strong motivation to learn how loops can be sped up through the use of parallelism, which is the focus of this module. We will start by learning how parallel counted-for loops can be conveniently expressed using forall and stream APIs in Java, and how these APIs can be used to parallelize a simple matrix multiplication program. We will also learn about the barrier construct for parallel loops, and illustrate its use with a simple iterative averaging program example. Finally, we will learn the importance of grouping/chunking parallel iterations to reduce overhead.

完成时间为 4 小时

7 个视频（总计 41 分钟）, 6 个阅读材料, 2 个测验

7 个视频

3.1 Parallel Loops5分钟

3.2 Parallel Matrix Multiplication4分钟

3.3 Barriers in Parallel Loops5分钟

3.4 Parallel One-Dimensional Iterative Averaging8分钟

3.5 Iteration Grouping/Chunking in Parallel Loops6分钟

Parallel Matrix Multiplication (Demo)4分钟

Parallel One-Dimensional Iterative Averaging (Demo)5分钟

6 个阅读材料

3.1 Lecture Summary10分钟

3.2 Lecture Summary10分钟

3.3 Lecture Summary10分钟

3.4 Lecture Summary10分钟

3.5 Lecture Summary10分钟

Mini Project 3: Parallelizing Matrix-Matrix Multiply Using Loop Parallelism10分钟

1 个练习

Module 3 Quiz30分钟

周

周 4

完成时间为 5 小时

Data flow Synchronization and Pipelining

Welcome to the last module of the course! In this module, we will wrap up our introduction to parallel programming by learning how data flow principles can be used to increase the amount of parallelism in a program. We will learn how Java’s Phaser API can be used to implement “fuzzy” barriers, and also “point-to-point” synchronizations as an optimization of regular barriers by revisiting the iterative averaging example. Finally, we will also learn how pipeline parallelism and data flow models can be expressed using Java APIs.

完成时间为 5 小时

7 个视频（总计 38 分钟）, 7 个阅读材料, 2 个测验

7 个视频

4.1 Split-phase Barriers with Java Phasers4分钟

4.2 Point-to-Point Sychronization with Phasers4分钟

4.3 One-Dimensional Iterative Averaging with Phasers4分钟

4.4 Pipeline Parallelism5分钟

4.5 Data Flow Parallelism5分钟

Phaser Examples6分钟

Pipeline & Data Flow Parallelism7分钟

7 个阅读材料

4.1 Lecture Summary10分钟

4.2 Lecture Summary10分钟

4.3 Lecture Summary10分钟

4.4 Lecture Summary10分钟

4.5 Lecture Summary10分钟

Mini Project 4: Using Phasers to Optimize Data-Parallel Applications10分钟

Exit Survey10分钟

1 个练习

Module 4 Quiz30分钟

完成时间为 20 分钟

Continue Your Journey with the Specialization "Parallel, Concurrent, and Distributed Programming in Java"

完成时间为 20 分钟

2 个视频（总计 10 分钟）, 1 个阅读材料

审阅

4.6

243 条评论

5 stars
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4 stars
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2 stars
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来自PARALLEL PROGRAMMING IN JAVA的热门评论

由 MB 提供Jan 27, 2018

Instructor is awesome. However instead of sharing ready libraries for parallelism (like forall loop) in section 3, native codes should be used for forall loops etc.

由 YY 提供Feb 27, 2019

This course explain the concepts in a great way but the exercises

needs to be more rich with problems to enhance our understanding to the concepts.

由 BK 提供Jul 19, 2020

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.

由 SD 提供May 25, 2020

I found the explanation of how to precisely code phasors lacking as the Thread stuff was already provided in sample code and never explained. Quite satisfied with the rest of the course though.

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关于 Parallel, Concurrent, and Distributed Programming in Java 专项课程

Parallel, concurrent, and distributed programming underlies software in multiple domains, ranging from biomedical research to financial services. This specialization is intended for anyone with a basic knowledge of sequential programming in Java, who is motivated to learn how to write parallel, concurrent and distributed programs. Through a collection of three courses (which may be taken in any order or separately), you will learn foundational topics in Parallelism, Concurrency, and Distribution. These courses will prepare you for multithreaded and distributed programming for a wide range of computer platforms, from mobile devices to cloud computing servers.

To see an overview video for this Specialization, click here! For an interview with two early-career software engineers on the relevance of parallel computing to their jobs, click here. Acknowledgments The instructor, Prof. Vivek Sarkar, would like to thank Dr. Max Grossman for his contributions to the mini-projects and other course material, Dr. Zoran Budimlic for his contributions to the quizzes, Dr. Max Grossman and Dr. Shams Imam for their contributions to the pedagogic PCDP library used in some of the mini-projects, and all members of the Rice Online team who contributed to the development of the course content (including Martin Calvi, Annette Howe, Seth Tyger, and Chong Zhou).

Parallel, Concurrent, and Distributed Programming in Java

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