About this course: 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).
Who is this class for: The course is targeted at an audience that is already familiar with sequential programming in Java, including a basic knowledge of Java 8 lambdas. In addition, we assume that each student has access to a laptop/desktop computer with a recent installation of Java 8. The course site includes instructions on how to obtain this installation, if needed.
Created by: Rice University
Taught by: Vivek Sarkar, Professor
Department of Computer Science
| Basic Info | Course 1 of 3 in the Parallel, Concurrent, and Distributed Programming in Java Specialization |
| Level | Intermediate |
| Commitment | Four weeks of study, 4-8 hours/week depending on past experience with sequential programming in Java |
| Language | English |
| Hardware Req | Access to a computer that can run Java 8 programs for optional course projects |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Welcome to the Course!
Welcome to Parallel Programming in Java! This course is designed as a three-part series and covers a theme or body of knowledge through various video lectures, demonstrations, and coding projects.
1 video, 5 readings
- Video: Course Welcome
- Reading: General Course Info
- Reading: Course Icon Legend
- Reading: Discussion Forum Guidelines
- Discussion Prompt: Get to know your classmates!
- Reading: Pre-Course Survey
- Reading: Mini Project 0: Setup
- Ungraded Programming: Mini Project 0 Submission
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.
7 videos, 6 readings
- Video: 1.1 Task Creation and Termination (Async, Finish)
- Reading: 1.1 Lecture Summary
- Video: 1.2 Tasks in Java's Fork/Join Framework
- Reading: 1.2 Lecture Summary
- Video: 1.3 Computation Graphs, Work, Span
- Reading: 1.3 Lecture Summary
- Video: 1.4 Multiprocessor Scheduling, Parallel Speedup
- Reading: 1.4 Lecture Summary
- Video: 1.5 Amdahl's Law
- Reading: 1.5 Lecture Summary
- Video: ReciprocalArraySum using Async-Finish (Demo)
- Video: ReciprocalArraySum using RecursiveAction's in Java's Fork/Join Framework (Demo)
- Reading: Mini Project 1: Reciprocal-Array-Sum using the Java Fork/Join Framework
Graded: Mini Project 1 Submission
Graded: Module 1 Quiz
WEEK 2
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.
7 videos, 6 readings
- Video: 2.1 Futures: Tasks with Return Values
- Reading: 2.1 Lecture Summary
- Video: 2.2 Futures in Java's Fork/Join Framework
- Reading: 2.2 Lecture Summary
- Video: 2.3 Memoization
- Reading: 2.3 Lecture Summary
- Video: 2.4 Java Streams
- Reading: 2.4 Lecture Summary
- Video: 2.5 Data Races and Determinism
- Reading: 2.5 Lecture Summary
- Video: ReciprocalArraySum using RecursiveTask's in Java's Fork/Join Framework (Demo)
- Video: Parallel List Processing Using Java Streams (Demo)
- Reading: Mini Project 2: Analyzing Student Statistics Using Java Parallel Streams
Graded: Mini Project 2 Submission
Graded: Module 2 Quiz
Talking to Two Sigma: Using it in the Field
Join Professor Vivek Sarkar as he talks with Two Sigma Managing Director, Jim Ward, and Software Engineers, Margaret Kelley and Jake Kornblau, at their downtown Houston, Texas office about the importance of parallel programming.
2 videos, 1 reading
- Reading: About these Talks
- Video: Industry Professional on Parallel, Concurrent, and Distributed Programming in Java - Jim Ward, Managing Director
- Video: Industry Professionals on Parallelism - Jake Kornblau and Margaret Kelley, Software Engineers
WEEK 3
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.
7 videos, 6 readings
- Video: 3.1 Parallel Loops
- Reading: 3.1 Lecture Summary
- Video: 3.2 Parallel Matrix Multiplication
- Reading: 3.2 Lecture Summary
- Video: 3.3 Barriers in Parallel Loops
- Reading: 3.3 Lecture Summary
- Video: 3.4 Parallel One-Dimensional Iterative Averaging
- Reading: 3.4 Lecture Summary
- Video: 3.5 Iteration Grouping/Chunking in Parallel Loops
- Reading: 3.5 Lecture Summary
- Video: Parallel Matrix Multiplication (Demo)
- Video: Parallel One-Dimensional Iterative Averaging (Demo)
- Reading: Mini Project 3: Parallelizing Matrix-Matrix Multiply Using Loop Parallelism
Graded: Mini Project 3 Submission
Graded: Module 3 Quiz
WEEK 4
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.
7 videos, 7 readings
- Video: 4.1 Split-phase Barriers with Java Phasers
- Reading: 4.1 Lecture Summary
- Video: 4.2 Point-to-Point Sychronization with Phasers
- Reading: 4.2 Lecture Summary
- Video: 4.3 One-Dimensional Iterative Averaging with Phasers
- Reading: 4.3 Lecture Summary
- Video: 4.4 Pipeline Parallelism
- Reading: 4.4 Lecture Summary
- Video: 4.5 Data Flow Parallelism
- Reading: 4.5 Lecture Summary
- Video: Phaser Examples
- Video: Pipeline & Data Flow Parallelism
- Reading: Mini Project 4: Using Phasers to Optimize Data-Parallel Applications
- Reading: Exit Survey
Graded: Mini Project 4 Submission
Graded: Module 4 Quiz
Continue Your Journey with the Specialization "Parallel, Concurrent, and Distributed Programming in Java"
The next two videos will showcase the importance of learning about Concurrent Programming and Distributed Programming in Java. Professor Vivek Sarkar will speak with industry professionals at Two Sigma about how the topics of our other two courses are utilized in the field.
2 videos, 1 reading
- Reading: Our Other Course Offerings
- Video: Industry Professional on Concurrency - Dr. Shams Imam, Software Engineer, Two Sigma
- Video: Industry Professional on Distribution - Dr. Eric Allen, Senior Vice President, Two Sigma
FAQs
How It Works
Coursework
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Help from Your Peers
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Certificates
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
Creators
Rice University
Rice University is consistently ranked among the top 20 universities in the U.S. and the top 100 in the world. Rice has highly respected schools of Architecture, Business, Continuing Studies, Engineering, Humanities, Music, Natural Sciences and Social Sciences and is home to the Baker Institute for Public Policy.
Pricing
| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
Ratings and Reviews
Great course for learning parallel programming with Java!
it'll be perfect if mini projects can deeper test what we've learned in the class.
The video lessons are short and well made with practical summaries. Parallelism is a must for every serious software engineer just as being good with algorithms and data structures. Re-watch the videos and summaries to help you do the mini-projects.
Coursera provides universal access to the world’s best education, partnering with top universities and organizations to offer courses online.
© 2017 Coursera Inc. All rights reserved.
RG
Great course!