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++).
Este curso faz parte do Programa de cursos integrados Parallel, Concurrent, and Distributed Programming in Java
Distributed Programming in Java
oferecido por
Programa de cursos integrados Parallel, Concurrent, and Distributed Programming in JavaUniversidade Rice
8,958 Inscrito
Informações sobre o curso
14,441 visualizações recentes
Habilidades que você terá
Distributed ComputingActor ModelParallel ComputingReactive Programming
Programa - O que você aprenderá com este curso
Semana
11 hora para concluir
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 vídeo (total de (Total 1 mín.) min), 5 leituras, 1 teste
1 vídeos
Course Welcome1min
5 leituras
General Course Info5min
Course Icon Legend2min
Discussion Forum Guidelines2min
Pre-Course Survey10min
Mini Project 0: Setup20min
4 horas para concluir
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.
6 vídeos (total de (Total 49 mín.) min), 6 leituras, 2 testes
6 videos
1.2 Hadoop Framework8min
1.3 Spark Framework11min
1.4 TF-IDF Example7min
1.5 Page Rank Example8min
Demonstration: Page Rank Algorithm in Spark4min
6 leituras
1.1 Lecture Summary5min
1.2 Lecture Summary5min
1.3 Lecture Summary5min
1.4 Lecture Summary5min
1.5 Lecture Summary5min
Mini Project 1: Page Rank with Spark15min
1 exercício prático
Module 1 Quiz30min
Semana
24 horas para concluir
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.
6 vídeos (total de (Total 43 mín.) min), 6 leituras, 2 testes
6 videos
2.2 Serialization/Deserialization9min
2.3 Remote Method Invocation6min
2.4 Multicast Sockets7min
2.5 Publish-Subscribe Model6min
Demonstration: File Server using Sockets4min
6 leituras
2.1 Lecture Summary5min
2.2 Lecture Summary5min
2.3 Lecture Summary5min
2.4 Lecture Summary5min
2.5 Lecture Summary5min
Mini Project 2: File Server15min
1 exercício prático
Module 2 Quiz30min
15 minutos para concluir
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.
2 vídeos (total de (Total 13 mín.) min), 1 leitura
2 videos
Industry Professional on Distribution - Dr. Eric Allen, Senior Vice President6min
1 leituras
About these Talks2min
Semana
34 horas para concluir
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.
6 vídeos (total de (Total 49 mín.) min), 6 leituras, 2 testes
6 videos
3.2 Point-to-Point Communication9min
3.3 Message Ordering and Deadlock8min
3.4 Non-Blocking Communications7min
3.5 Collective Communication7min
Demonstration: Distributed Matrix Multiply using Message Passing9min
6 leituras
3.1 Lecture Summary7min
3.2 Lecture Summary5min
3.3 Lecture Summary5min
3.4 Lecture Summary5min
3.5 Lecture Summary5min
Mini Project 3: Matrix Multiply in MPI15min
1 exercício prático
Module 3 Quiz30min
Semana
44 horas para concluir
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.
6 vídeos (total de (Total 44 mín.) min), 7 leituras, 2 testes
6 videos
4.2 Multithreaded Servers6min
4.3 MPI and Threading7min
4.4 Distributed Actors8min
4.5 Distributed Reactive Programming7min
Demonstration: Parallel File Server using Multithreading and Sockets3min
7 leituras
4.1 Lecture Summary5min
4.2 Lecture Summary5min
4.3 Lecture Summary10min
4.4 Lecture Summary5min
4.5 Lecture Summary5min
Mini Project 4: Multi-Threaded File Server15min
Exit Survey10min
1 exercício prático
Module 4 Quiz30min
20 minutos para concluir
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.
2 vídeos (total de (Total 10 mín.) min), 1 leitura
2 videos
Industry Professional on Concurrency - Dr. Shams Imam, Software Engineer, Two Sigma3min
1 leituras
Our Other Course Offerings10min
25%
comecei uma nova carreira após concluir estes cursos
29%
consegui um benefício significativo de carreira com este curso
33%
recebi um aumento ou promoção
Melhores avaliações
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.
Excellent course! Vivek is an excellent instructor as well. I appreciate having taken the opportunity to learn from him.
Instrutores
Vivek Sarkar
ProfessorDepartment of Computer Science
Sobre Universidade 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.
Sobre o Programa de cursos integrados 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).
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
Ao se inscrever para um Certificado, você terá acesso a todos os vídeos, testes e tarefas de programação (se aplicável). Tarefas avaliadas pelos colegas apenas podem ser enviadas e avaliadas após o início da sessão. Caso escolha explorar o curso sem adquiri-lo, talvez você não consiga acessar certas tarefas.
O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
Qual é a política de reembolso?
Existe algum auxílio financeiro disponível?
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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