This course is part of the Parallel, Concurrent, and Distributed Programming in Java Specialization

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Vivek Sarkar

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Parallel, Concurrent, and Distributed Programming in Java SpecializationRice University

About this Course

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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).

Flexible deadlines

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Shareable Certificate

Earn a Certificate upon completion

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Coursera Labs

Includes hands on learning projects.

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Course 1 of 3 in the

Parallel, Concurrent, and Distributed Programming in Java Specialization

Intermediate Level

Approx. 19 hours to complete

English

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Skills you will gain

Dataflow
Parallel Computing
Java Concurrency
Data Parallelism

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Course 1 of 3 in the

Parallel, Concurrent, and Distributed Programming in Java Specialization

Intermediate Level

Approx. 19 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.82/5 (113 Ratings)

Vivek Sarkar

Professor

Department of Computer Science

53,960 Learners

3 Courses

Offered by

Rice University

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Syllabus - What you will learn from this course

Content Rating94%(4,342 ratings)

Week1

Week 1

1 hour to complete

Welcome to the Course!

1 hour to complete

1 video (Total 1 min), 5 readings, 1 quiz

4 hours to complete

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.

4 hours to complete

7 videos (Total 42 min), 6 readings, 2 quizzes

7 videos

1.1 Task Creation and Termination (Async, Finish)4m1.2 Tasks in Java's Fork/Join Framework5m1.3 Computation Graphs, Work, Span7m1.4 Multiprocessor Scheduling, Parallel Speedup8m1.5 Amdahl's Law5mReciprocalArraySum using Async-Finish (Demo)4mReciprocalArraySum using RecursiveAction's in Java's Fork/Join Framework (Demo)5m

6 readings

1.1 Lecture Summary5m1.2 Lecture Summary5m1.3 Lecture Summary5m1.4 Lecture Summary5m1.5 Lecture Summary5mMini Project 1: Reciprocal-Array-Sum using the Java Fork/Join Framework10m

1 practice exercise

Module 1 Quiz30m

Week2

Week 2

4 hours to complete

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.

4 hours to complete

7 videos (Total 40 min), 6 readings, 2 quizzes

7 videos

2.1 Futures: Tasks with Return Values6m2.2 Futures in Java's Fork/Join Framework5m2.3 Memoization6m2.4 Java Streams5m2.5 Data Races and Determinism9mReciprocalArraySum using RecursiveTask's in Java's Fork/Join Framework (Demo)3mParallel List Processing Using Java Streams (Demo)4m

6 readings

2.1 Lecture Summary10m2.2 Lecture Summary10m2.3 Lecture Summary10m2.4 Lecture Summary10m2.5 Lecture Summary10mMini Project 2: Analyzing Student Statistics Using Java Parallel Streams10m

1 practice exercise

Module 2 Quiz30m

23 minutes to complete

Talking to Two Sigma: Using it in the Field

23 minutes to complete

2 videos (Total 13 min), 1 reading

Week3

Week 3

4 hours to complete

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.

4 hours to complete

7 videos (Total 41 min), 6 readings, 2 quizzes

7 videos

3.1 Parallel Loops5m3.2 Parallel Matrix Multiplication4m3.3 Barriers in Parallel Loops5m3.4 Parallel One-Dimensional Iterative Averaging8m3.5 Iteration Grouping/Chunking in Parallel Loops6mParallel Matrix Multiplication (Demo)4mParallel One-Dimensional Iterative Averaging (Demo)5m

6 readings

3.1 Lecture Summary10m3.2 Lecture Summary10m3.3 Lecture Summary10m3.4 Lecture Summary10m3.5 Lecture Summary10mMini Project 3: Parallelizing Matrix-Matrix Multiply Using Loop Parallelism10m

1 practice exercise

Module 3 Quiz30m

Week4

Week 4

5 hours to complete

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.

5 hours to complete

7 videos (Total 38 min), 7 readings, 2 quizzes

7 videos

4.1 Split-phase Barriers with Java Phasers4m4.2 Point-to-Point Sychronization with Phasers4m4.3 One-Dimensional Iterative Averaging with Phasers4m4.4 Pipeline Parallelism5m4.5 Data Flow Parallelism5mPhaser Examples6mPipeline & Data Flow Parallelism7m

7 readings

4.1 Lecture Summary10m4.2 Lecture Summary10m4.3 Lecture Summary10m4.4 Lecture Summary10m4.5 Lecture Summary10mMini Project 4: Using Phasers to Optimize Data-Parallel Applications10mExit Survey10m

1 practice exercise

Module 4 Quiz30m

20 minutes to complete

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

20 minutes to complete

2 videos (Total 10 min), 1 reading

Reviews

4.6

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TOP REVIEWS FROM PARALLEL PROGRAMMING IN JAVA

by RADec 26, 2017

Very relevant concepts, explained beautifully by a very experienced professor. The assignments were good, but they could involve more programming for more practice!

by JLDec 13, 2019

It was actually quite fun. I particularly enjoyed the Two Sigma interviews, which gave me a better understanding about how the course concepts are applied in business.

by BKJul 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.

by VZNov 6, 2017

Very useful course about parallel programming theory and practice.

Could be less examples with custom libraries instead of standard java features looking to practical usage at work.

View all reviews

About the Parallel, Concurrent, and Distributed Programming in Java Specialization

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

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

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Parallel Programming in Java

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