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

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478 ratings

Vivek Sarkar

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Offered By

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 Distributed Programming in the context of Java 8.  Distributed programming enables developers to use multiple nodes in a data center to increase throughput and/or reduce latency of selected applications.  By the end of this course, you will learn how to use popular distributed programming frameworks for Java programs, including Hadoop, Spark, Sockets, Remote Method Invocation (RMI), Multicast Sockets, Kafka, Message Passing Interface (MPI), as well as different approaches to combine distribution with multithreading.
Why take this course?

•	All data center servers are organized as collections of distributed servers, and it is important for you to also learn how to use multiple servers for increased bandwidth and reduced latency.
•	In addition to learning specific frameworks for distributed programming, this course will teach you how to integrate multicore and distributed parallelism in a unified approach.
•	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:

•	Distributed map-reduce programming in Java using the Hadoop and Spark frameworks
•	Client-server programming using Java's Socket and Remote Method Invocation (RMI) interfaces
•	Message-passing programming in Java using the Message Passing Interface (MPI)
•	Approaches to combine distribution with multithreading, including processes and threads, distributed actors, and reactive programming

Mastery of these concepts will enable you to immediately apply them in the context of distributed Java programs, and will also provide the foundation for mastering other distributed programming frameworks that you may encounter in the future  (e.g., in Scala or C++).

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 3 of 3 in the

Parallel, Concurrent, and Distributed Programming in Java Specialization

Intermediate Level

Approx. 17 hours to complete

English

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Skills you will gain

Distributed Computing
Actor Model
Parallel Computing
Reactive Programming

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 3 of 3 in the

Parallel, Concurrent, and Distributed Programming in Java Specialization

Intermediate Level

Approx. 17 hours to complete

English

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

Try Coursera for Business

Instructor

Instructor rating

4.75/5 (44 Ratings)

Vivek Sarkar

Professor

Department of Computer Science

53,877 Learners

3 Courses

Offered by

Rice University

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Syllabus - What you will learn from this course

Content Rating94%(1,558 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

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.

4 hours to complete

6 videos (Total 49 min), 6 readings, 2 quizzes

Week2

Week 2

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.

4 hours to complete

6 videos (Total 43 min), 6 readings, 2 quizzes

15 minutes to complete

Talking to Two Sigma: Using it in the Field

15 minutes to complete

2 videos (Total 13 min), 1 reading

Week3

Week 3

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.

4 hours to complete

6 videos (Total 49 min), 6 readings, 2 quizzes

6 videos

3.1 Single Program Multiple Data (SPMD) model7m3.2 Point-to-Point Communication9m3.3 Message Ordering and Deadlock8m3.4 Non-Blocking Communications7m3.5 Collective Communication7mDemonstration: Distributed Matrix Multiply using Message Passing9m

6 readings

3.1 Lecture Summary7m3.2 Lecture Summary5m3.3 Lecture Summary5m3.4 Lecture Summary5m3.5 Lecture Summary5mMini Project 3: Matrix Multiply in MPI15m

1 practice exercise

Module 3 Quiz30m

Week4

Week 4

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.

4 hours to complete

6 videos (Total 44 min), 7 readings, 2 quizzes

6 videos

4.1 Processes and Threads9m4.2 Multithreaded Servers6m4.3 MPI and Threading7m4.4 Distributed Actors8m4.5 Distributed Reactive Programming7mDemonstration: Parallel File Server using Multithreading and Sockets3m

7 readings

4.1 Lecture Summary5m4.2 Lecture Summary5m4.3 Lecture Summary10m4.4 Lecture Summary5m4.5 Lecture Summary5mMini Project 4: Multi-Threaded File Server15mExit 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

77 reviews

5 stars
69.31%
4 stars
22.75%
3 stars
5.01%
2 stars
0.83%
1 star
2.08%

TOP REVIEWS FROM DISTRIBUTED PROGRAMMING IN JAVA

by SNMay 1, 2020

The concepts taught were clear and precise which helped me with an ongoing project. It would have been really better if the mini-projects were a bit more complicated.

by APApr 23, 2020

Great experience and all the lectures are really interesting and the concepts are precise and perfect.

by TMMay 4, 2018

First two courses are better. This one is quick and a little vague

by NKSep 4, 2017

It forced me to recall OpenMP and MPI. Thought it's already dead in favor to IMDG, spark, upc and so on.

Practical tasks - miniprojects - was too easy to implement. Need more complex tasks.

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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.
Do I need to buy a textbook?
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.
How does the Multicore Programming in Java: Parallelism course relate to the Multicore Programming in Java: Concurrency course?
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.

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Distributed Programming in Java

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