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.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Parallel, Concurrent, and Distributed Programming in Java
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Untertitel: Arabischer Raum, Französisch, Portugiesisch (europäisch), Italienisch, Vietnamesisch, Deutsch, Russisch, Englisch, Spanisch
Kompetenzen, die Sie erwerben
- 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
100Â % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Stufe „Mittel“
Ca. 18 Stunden zum Abschließen
Englisch
Untertitel: Arabischer Raum, Französisch, Portugiesisch (europäisch), Italienisch, Vietnamesisch, Deutsch, Russisch, Englisch, Spanisch
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Lehrplan - Was Sie in diesem Kurs lernen werden
1 Stunde zum Abschließen
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.
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
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
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.
15 Minuten zum Abschließen
2 Videos (Gesamt 13 min), 1 Lektüre
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
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
20 Minuten zum Abschließen
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.
20 Minuten zum Abschließen
2 Videos (Gesamt 10 min), 1 Lektüre
Bewertungen
- 5 stars69,13Â %
- 4 stars22,83Â %
- 3 stars5,07Â %
- 2 stars0,84Â %
- 1 star2,11Â %
Top-Bewertungen von DISTRIBUTED PROGRAMMING IN JAVA
von PB27. Nov. 2020
Brilliant course. Prof Sarkar is wonderful as always.
von JC4. Juli 2020
Great lectures. I really learned a lot about distributed computing.
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 MD2. Mai 2022
Learnt so much, I've going to do such good things at work. :)
Ãœber 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.
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