About this Course
4.4
166 ratings
24 reviews
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Hours to complete

Approx. 12 hours to complete

Suggested: Four weeks of study, 4-8 hours/week depending on past experience with sequential programming in Java...
Available languages

English

Subtitles: English

Skills you will gain

Distributed ComputingActor ModelParallel ComputingReactive Programming
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Intermediate Level

Intermediate Level

Hours to complete

Approx. 12 hours to complete

Suggested: Four weeks of study, 4-8 hours/week depending on past experience with sequential programming in Java...
Available languages

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
Hours to complete
1 hour to complete

Welcome to the Course!

Welcome to Distributed 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....
Reading
1 video (Total 1 min), 5 readings, 1 quiz
Video1 video
Reading5 readings
General Course Info5m
Course Icon Legend2m
Discussion Forum Guidelines2m
Pre-Course Survey10m
Mini Project 0: Setup20m
Hours to complete
4 hours to complete

DISTRIBUTED MAP REDUCE

In this module, we will learn about the MapReduce paradigm, and how it can be used to write distributed programs that analyze data represented as key-value pairs. A MapReduce program is defined via user-specified map and reduce functions, and we will learn how to write such programs in the Apache Hadoop and Spark projects. TheMapReduce paradigm can be used to express a wide range of parallel algorithms. One example that we will study is computation of the TermFrequency – Inverse Document Frequency (TF-IDF) statistic used in document mining; this algorithm uses a fixed (non-iterative) number of map and reduce operations. Another MapReduce example that we will study is parallelization of the PageRank algorithm. This algorithm is an example of iterative MapReduce computations, and is also the focus of the mini-project associated with this module....
Reading
6 videos (Total 49 min), 6 readings, 2 quizzes
Video6 videos
1.2 Hadoop Framework8m
1.3 Spark Framework11m
1.4 TF-IDF Example7m
1.5 Page Rank Example8m
Demonstration: Page Rank Algorithm in Spark4m
Reading6 readings
1.1 Lecture Summary5m
1.2 Lecture Summary5m
1.3 Lecture Summary5m
1.4 Lecture Summary5m
1.5 Lecture Summary5m
Mini Project 1: Page Rank with Spark15m
Quiz1 practice exercise
Module 1 Quiz30m
Week
2
Hours to complete
4 hours to complete

CLIENT-SERVER PROGRAMMING

In this module, we will learn about client-server programming, and how distributed Java applications can communicate with each other using sockets. Since communication via sockets occurs at the level of bytes, we will learn how to serialize objects into bytes in the sender process and to deserialize bytes into objects in the receiver process. Sockets and serialization provide the necessary background for theFile Server mini-project associated with this module. We will also learn about Remote Method Invocation (RMI), which extends the notion of method invocation in a sequential program to a distributed programming setting. Likewise, we will learn about multicast sockets,which generalize the standard socket interface to enable a sender to send the same message to a specified set of receivers; this capability can be very useful for a number of applications, including news feeds,video conferencing, and multi-player games. Finally, we will learn about distributed publish-subscribe applications, and how they can be implemented using the Apache Kafka framework....
Reading
6 videos (Total 43 min), 6 readings, 2 quizzes
Video6 videos
2.2 Serialization/Deserialization9m
2.3 Remote Method Invocation6m
2.4 Multicast Sockets7m
2.5 Publish-Subscribe Model6m
Demonstration: File Server using Sockets4m
Reading6 readings
2.1 Lecture Summary5m
2.2 Lecture Summary5m
2.3 Lecture Summary5m
2.4 Lecture Summary5m
2.5 Lecture Summary5m
Mini Project 2: File Server15m
Quiz1 practice exercise
Module 2 Quiz30m
Hours to complete
15 minutes to complete

Talking to Two Sigma: Using it in the Field

Join Professor Vivek Sarkar as he talks with Two Sigma Managing Director, Jim Ward, and Senior Vice President, Dr. Eric Allen at their downtown Houston, Texas office about the importance of distributed programming....
Reading
2 videos (Total 13 min), 1 reading
Video2 videos
Industry Professional on Distribution - Dr. Eric Allen, Senior Vice President6m
Reading1 reading
About these Talks2m
Week
3
Hours to complete
4 hours to complete

MESSAGE PASSING

In this module, we will learn how to write distributed applications in the Single Program Multiple Data (SPMD) model, specifically by using the Message Passing Interface (MPI) library. MPI processes can send and receive messages using primitives for point-to-point communication, which are different in structure and semantics from message-passing with sockets. We will also learn about the message ordering and deadlock properties of MPI programs. Non-blocking communications are an interesting extension of point-to-point communications, since they can be used to avoid delays due to blocking and to also avoid deadlock-related errors. Finally, we will study collective communication, which can involve multiple processes in a manner that is more powerful than multicast and publish-subscribe operations. The knowledge of MPI gained in this module will be put to practice in the mini-project associated with this module on implementing a distributed matrix multiplication program in MPI....
Reading
6 videos (Total 49 min), 6 readings, 2 quizzes
Video6 videos
3.2 Point-to-Point Communication9m
3.3 Message Ordering and Deadlock8m
3.4 Non-Blocking Communications7m
3.5 Collective Communication7m
Demonstration: Distributed Matrix Multiply using Message Passing9m
Reading6 readings
3.1 Lecture Summary7m
3.2 Lecture Summary5m
3.3 Lecture Summary5m
3.4 Lecture Summary5m
3.5 Lecture Summary5m
Mini Project 3: Matrix Multiply in MPI15m
Quiz1 practice exercise
Module 3 Quiz30m
Week
4
Hours to complete
4 hours to complete

COMBINING DISTRIBUTION AND MULTITHREADING

In this module, we will study the roles of processes and threads as basic building blocks of parallel, concurrent, and distributed Java programs. With this background, we will then learn how to implement multithreaded servers for increased responsiveness in distributed applications written using sockets, and apply this knowledge in the mini-project on implementing a parallel file server using both multithreading and sockets. An analogous approach can also be used to combine MPI and multithreading, so as to improve the performance of distributed MPI applications. Distributed actors serve as yet another example of combining distribution and multithreading. A notable property of the actor model is that the same high-level constructs can be used to communicate among actors running in the same process and among actors in different processes; the difference between the two cases depends on the application configuration, rather the application code. Finally, we will learn about the reactive programming model,and its suitability for implementing distributed service oriented architectures using asynchronous events....
Reading
6 videos (Total 44 min), 7 readings, 2 quizzes
Video6 videos
4.2 Multithreaded Servers6m
4.3 MPI and Threading7m
4.4 Distributed Actors8m
4.5 Distributed Reactive Programming7m
Demonstration: Parallel File Server using Multithreading and Sockets3m
Reading7 readings
4.1 Lecture Summary5m
4.2 Lecture Summary5m
4.3 Lecture Summary10m
4.4 Lecture Summary5m
4.5 Lecture Summary5m
Mini Project 4: Multi-Threaded File Server15m
Exit Survey10m
Quiz1 practice exercise
Module 4 Quiz30m
Hours to complete
20 minutes to complete

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

The next two videos will showcase the importance of learning about Parallel Programming and Concurrent 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....
Reading
2 videos (Total 10 min), 1 reading
Video2 videos
Industry Professional on Concurrency - Dr. Shams Imam, Software Engineer, Two Sigma3m
Reading1 reading
Our Other Course Offerings10m
4.4
24 ReviewsChevron Right
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50%

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Top Reviews

By DHSep 17th 2017

Great course. The first programming assignment was challenging and well worth the time invested, I would recommend it for anyone that wants to learn parallel programming in Java.

By FFJan 24th 2018

Excellent course! Vivek is an excellent instructor as well. I appreciate having taken the opportunity to learn from him.

Instructor

Avatar

Vivek Sarkar

Professor
Department of Computer Science

About 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....

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

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • 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.

  • No. The lecture videos, demonstrations and quizzes will be sufficient to enable you to complete this course. Students who enroll in the course and are interesting in receiving a certificate will also have access to a supplemental coursebook with additional technical details.

  • Multicore Programming in Java: Parallelism and Multicore Programming in Java: Concurrency cover complementary aspects of multicore programming, and can be taken in any order. The Parallelism course covers the fundamentals of using parallelism to make applications run faster by using multiple processors at the same time. The Concurrency course covers the fundamentals of how parallel tasks and threads correctly mediate concurrent use of shared resources such as shared objects, network resources, and file systems.

More questions? Visit the Learner Help Center.