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Parallel Programming in Java

Approx. 19 hours to complete

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Course Summary

Learn how to write parallel programs in Java using threads and the java.util.concurrent package. This course covers the basic concepts of parallel programming and teaches you how to use them in practice.

Key Learning Points

Understand the fundamentals of parallel programming and how to apply them in Java
Learn how to use threads and synchronization to write efficient and scalable parallel programs
Explore different approaches for parallelization, such as fork-join and map-reduce

Job Positions & Salaries of people who have taken this course might have

Java Developer
- USA: $85,000 - $135,000
- India: INR 6,00,000 - INR 12,00,000
- Spain: €30,000 - €45,000
Parallel Computing Specialist
- USA: $120,000 - $175,000
- India: INR 12,00,000 - INR 20,00,000
- Spain: €40,000 - €60,000
Software Engineer (Parallel Programming)
- USA: $100,000 - $150,000
- India: INR 8,00,000 - INR 14,00,000
- Spain: €35,000 - €50,000

Learning Outcomes

Understand the basic concepts of parallel programming
Be able to write efficient and scalable parallel programs using Java
Explore different approaches for parallelization and understand their trade-offs

Prerequisites or good to have knowledge before taking this course

Basic knowledge of Java programming
Familiarity with object-oriented programming concepts

Course Difficulty Level

Intermediate

Course Format

Online self-paced course
Video lectures
Quizzes and assignments

Similar Courses

Parallel Computing
Advanced Data Structures in Java
Distributed Systems

Related Education Paths

Notable People in This Field

Brian Goetz
Doug Lea
Joshua Bloch

Related Books

Description

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).

Outline

Welcome to the Course!
Course Welcome
General Course Info
Course Icon Legend
Discussion Forum Guidelines
Pre-Course Survey
Mini Project 0: Setup

Task Parallelism
1.1 Task Creation and Termination (Async, Finish)
1.2 Tasks in Java's Fork/Join Framework
1.3 Computation Graphs, Work, Span
1.4 Multiprocessor Scheduling, Parallel Speedup
1.5 Amdahl's Law
ReciprocalArraySum using Async-Finish (Demo)
ReciprocalArraySum using RecursiveAction's in Java's Fork/Join Framework (Demo)
1.1 Lecture Summary
1.2 Lecture Summary
1.3 Lecture Summary
1.4 Lecture Summary
1.5 Lecture Summary
Mini Project 1: Reciprocal-Array-Sum using the Java Fork/Join Framework
Module 1 Quiz

Functional Parallelism
2.1 Futures: Tasks with Return Values
2.2 Futures in Java's Fork/Join Framework
2.3 Memoization
2.4 Java Streams
2.5 Data Races and Determinism
ReciprocalArraySum using RecursiveTask's in Java's Fork/Join Framework (Demo)
Parallel List Processing Using Java Streams (Demo)
2.1 Lecture Summary
2.2 Lecture Summary
2.3 Lecture Summary
2.4 Lecture Summary
2.5 Lecture Summary
Mini Project 2: Analyzing Student Statistics Using Java Parallel Streams
Module 2 Quiz

Talking to Two Sigma: Using it in the Field
Industry Professional on Parallel, Concurrent, and Distributed Programming in Java - Jim Ward, Managing Director
Industry Professionals on Parallelism - Jake Kornblau and Margaret Kelley, Software Engineers
About these Talks

Loop Parallelism
3.1 Parallel Loops
3.2 Parallel Matrix Multiplication
3.3 Barriers in Parallel Loops
3.4 Parallel One-Dimensional Iterative Averaging
3.5 Iteration Grouping/Chunking in Parallel Loops
Parallel Matrix Multiplication (Demo)
Parallel One-Dimensional Iterative Averaging (Demo)
3.1 Lecture Summary
3.2 Lecture Summary
3.3 Lecture Summary
3.4 Lecture Summary
3.5 Lecture Summary
Mini Project 3: Parallelizing Matrix-Matrix Multiply Using Loop Parallelism
Module 3 Quiz

Data flow Synchronization and Pipelining
4.1 Split-phase Barriers with Java Phasers
4.2 Point-to-Point Sychronization with Phasers
4.3 One-Dimensional Iterative Averaging with Phasers
4.4 Pipeline Parallelism
4.5 Data Flow Parallelism
Phaser Examples
Pipeline & Data Flow Parallelism
4.1 Lecture Summary
4.2 Lecture Summary
4.3 Lecture Summary
4.4 Lecture Summary
4.5 Lecture Summary
Mini Project 4: Using Phasers to Optimize Data-Parallel Applications
Exit Survey
Module 4 Quiz

Continue Your Journey with the Specialization "Parallel, Concurrent, and Distributed Programming in Java"
Industry Professional on Concurrency - Dr. Shams Imam, Software Engineer, Two Sigma
Industry Professional on Distribution - Dr. Eric Allen, Senior Vice President, Two Sigma
Our Other Course Offerings

Summary of User Reviews

Key Aspect Users Liked About This Course

The course provides a comprehensive introduction to parallel programming in Java

Pros from User Reviews

In-depth coverage of parallel programming concepts and techniques
Hands-on programming assignments that reinforce learning
Engaging and knowledgeable instructors
Flexible schedule allows for self-paced learning
Excellent resources and support from the course staff

Cons from User Reviews

Some users found the programming assignments challenging
The course may be overwhelming for beginners with no prior programming experience
The course requires a significant time commitment
The course material may be too technical for some users
The course does not cover parallel programming in other programming languages

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