Dieser Kurs ist Teil der Spezialisierung Spezialisierung Parallel, Concurrent, and Distributed Programming in Java

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Vivek Sarkar

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Spezialisierung Parallel, Concurrent, and Distributed Programming in JavaRice University

Über diesen Kurs

11.555 kürzliche Aufrufe

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++).

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Kurs 3 von 3 in

Spezialisierung Parallel, Concurrent, and Distributed Programming in Java

Stufe „Mittel“

Ca. 18 Stunden zum Abschließen

Englisch

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Distributed Computing
Actor Model
Parallel Computing
Reactive Programming

Flexible Fristen

Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.

Zertifikat zur Vorlage

Erhalten Sie nach Abschluss ein Zertifikat

100 % online

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Kurs 3 von 3 in

Spezialisierung Parallel, Concurrent, and Distributed Programming in Java

Stufe „Mittel“

Ca. 18 Stunden zum Abschließen

Englisch

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Dozent

Dozentenbewertung

4,75/5 (44 Bewertungen)

Vivek Sarkar

Professor

Department of Computer Science

53.152 Lernende

3 Kurse

von

Rice University

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Lehrplan - Was Sie in diesem Kurs lernen werden

Inhaltsbewertung94%(1,551 Bewertungen)

Woche

Woche 1

1 Stunde zum Abschließen

Welcome to the Course!

1 Stunde zum Abschließen

1 Video (Gesamt 1 min), 5 Lektüren, 1 Quiz

4 Stunden zum Abschließen

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 Stunden zum Abschließen

6 Videos (Gesamt 49 min), 6 Lektüren, 2 Quiz

Woche

Woche 2

4 Stunden zum Abschließen

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 Stunden zum Abschließen

6 Videos (Gesamt 43 min), 6 Lektüren, 2 Quiz

15 Minuten zum Abschließen

Talking to Two Sigma: Using it in the Field

15 Minuten zum Abschließen

2 Videos (Gesamt 13 min), 1 Lektüre

Woche

Woche 3

4 Stunden zum Abschließen

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 Stunden zum Abschließen

6 Videos (Gesamt 49 min), 6 Lektüren, 2 Quiz

6 Videos

3.1 Single Program Multiple Data (SPMD) model7m

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

6 Lektüren

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

1 praktische Übung

Module 3 Quiz30m

Woche

Woche 4

4 Stunden zum Abschließen

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 Stunden zum Abschließen

6 Videos (Gesamt 44 min), 7 Lektüren, 2 Quiz

6 Videos

4.1 Processes and Threads9m

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

7 Lektüren

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

1 praktische Übung

Module 4 Quiz30m

20 Minuten zum Abschließen

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

20 Minuten zum Abschließen

2 Videos (Gesamt 10 min), 1 Lektüre

Bewertungen

4.6

77 Bewertungen

5 stars
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4 stars
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3 stars
5,02 %
2 stars
0,83 %
1 star
2,09 %

Top-Bewertungen von DISTRIBUTED PROGRAMMING IN JAVA

von DH16. Sep. 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.

von BM30. Okt. 2020

The course was awesome. I learnt a lot about distribution programming and it will better my performance personally and in the job industry. Thank you.

von ML31. Dez. 2020

The hints and tutorial for mini projects in this section can be reduced a little bit.

von NK4. Sep. 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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Über den Spezialisierung Parallel, Concurrent, and Distributed Programming in Java

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

Häufig gestellte Fragen

Wann erhalte ich Zugang zu den Vorträgen und Aufgaben?
Der Zugang zu Vorlesungen und Aufgaben hängt von der Art Ihrer Anmeldung ab. Wenn Sie einen Kurs im Gastmodus belegen, können Sie die meisten Kursmaterialien kostenlos einsehen. Um auf benotete Aufgaben zuzugreifen und ein Zertifikat zu erhalten, müssen Sie während oder nach Ihrer Gastphase das Zertifikat erwerben. Wenn Sie die Gastoption nicht sehen:
Der Kurs bietet möglicherweise keine Gastoption an. Sie können stattdessen eine kostenlose Testversion ausprobieren oder finanzielle Unterstützung beantragen.
Der Kurs kann stattdessen "Vollständiger Kurs ohne Zertifikat" anbieten. Mit dieser Option können Sie alle Kursmaterialien einsehen, die erforderlichen Aufgaben einreichen und eine Endnote erhalten. Dies bedeutet auch, dass Sie kein Zertifikat erwerben können.
Was bekomme ich, wenn ich diese Spezialisierung abonniere?
Wenn Sie sich für den Kurs anmelden, erhalten Sie Zugriff auf alle Kurse der Spezialisierung und Sie erhalten nach Abschluss aller Arbeiten ein Zertifikat. Ihr elektronisches Zertifikat wird zu Ihrer Seite „Errungenschaften“ hinzugefügt – von dort können Sie Ihr Zertifikat ausdrucken oder es zu Ihrem LinkedIn Profil hinzufügen. Wenn Sie nur lesen und den Inhalt des Kurses anzeigen möchten, können Sie kostenlos als Gast an dem Kurs teilnehmen.
Ist finanzielle Unterstützung möglich?
Ja. Für ausgewählte Lernprogramme können Sie finanzielle Unterstützung oder ein Stipendium beantragen, wenn Sie sich die Anmeldegebühr nicht leisten können. Wenn für das von Ihnen gewählte Lernprogramm finanzielle Unterstützung oder ein Stipendium verfügbar ist, wird auf der Beschreibungsseite ein Link zu einem solchen Antrag angezeigt.
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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