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.
Este curso forma parte de Programa Especializado - Parallel, Concurrent, and Distributed Programming in Java
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Acerca de este Curso
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Resultados profesionales del estudiante
25%
comenzó una nueva carrera después de completar estos cursos
22%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
25%
consiguió un aumento de sueldo o ascenso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Aprox. 18 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Chino (simplificado), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Turco (Turkish), Inglés (English), Español (Spanish)
Habilidades que obtendrás
Distributed ComputingActor ModelParallel ComputingReactive Programming
Resultados profesionales del estudiante
25%
comenzó una nueva carrera después de completar estos cursos
22%
consiguió un beneficio tangible en su carrera profesional gracias a este curso
25%
consiguió un aumento de sueldo o ascenso
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Aprox. 18 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Chino (simplificado), Italiano, Vietnamita, Coreano, Alemán (German), Ruso (Russian), Turco (Turkish), Inglés (English), Español (Spanish)
ofrecido por
Universidad Rice
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.
Programa - Qué aprenderás en este curso
1 hora para completar
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 hora para completar
1 video (Total 1 minutos), 5 lecturas, 1 cuestionario
1 video
5 lecturas
General Course Info5m
Course Icon Legend2m
Discussion Forum Guidelines2m
Pre-Course Survey10m
Mini Project 0: Setup20m
4 horas para completar
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 horas para completar
6 videos (Total 49 minutos), 6 lecturas, 2 cuestionarios
6 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
6 lecturas
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
1 ejercicio de práctica
Module 1 Quiz30m
4 horas para completar
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 horas para completar
6 videos (Total 43 minutos), 6 lecturas, 2 cuestionarios
6 videos
2.2 Serialization/Deserialization9m
2.3 Remote Method Invocation6m
2.4 Multicast Sockets7m
2.5 Publish-Subscribe Model6m
Demonstration: File Server using Sockets4m
6 lecturas
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
1 ejercicio de práctica
Module 2 Quiz30m
15 minutos para completar
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 minutos para completar
2 videos (Total 13 minutos), 1 lectura
2 videos
Industry Professional on Distribution - Dr. Eric Allen, Senior Vice President6m
1 lectura
About these Talks2m
4 horas para completar
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 horas para completar
6 videos (Total 49 minutos), 6 lecturas, 2 cuestionarios
6 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
6 lecturas
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 ejercicio de práctica
Module 3 Quiz30m
4 horas para completar
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 horas para completar
6 videos (Total 44 minutos), 7 lecturas, 2 cuestionarios
6 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
7 lecturas
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 ejercicio de práctica
Module 4 Quiz30m
20 minutos para completar
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 minutos para completar
2 videos (Total 10 minutos), 1 lectura
2 videos
Industry Professional on Concurrency - Dr. Shams Imam, Software Engineer, Two Sigma3m
1 lectura
Our Other Course Offerings10m
Reseñas
Principales reseñas sobre DISTRIBUTED PROGRAMMING IN JAVA
por NK4 de sep. de 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.
por DH16 de sep. de 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.
por SN1 de may. de 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.
por AM30 de may. de 2020
Very well structured and presented course! Very useful material with good relevant examples! Thank you, Prof. Sarkar for this Distributed Programming in Java course!
Acerca de Programa especializado: 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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