Este curso faz parte do Programa de cursos integrados Parallel, Concurrent, and Distributed Programming in Java

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477 classificações

Vivek Sarkar

21.076 já se inscreveram

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Programa de cursos integrados Parallel, Concurrent, and Distributed Programming in JavaUniversidade Rice

Informações sobre o curso

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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++).

Prazos flexíveis

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Curso 3 de 3 no

Programa de cursos integrados Parallel, Concurrent, and Distributed Programming in Java

Nível intermediário

Aprox. 18 horas para completar

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Habilidades que você terá

Distributed Computing
Actor Model
Parallel Computing
Reactive Programming

Prazos flexíveis

Redefinir os prazos de acordo com sua programação.

Certificados compartilháveis

Tenha o certificado após a conclusão

100% on-line

Comece imediatamente e aprenda em seu próprio cronograma.

Curso 3 de 3 no

Programa de cursos integrados Parallel, Concurrent, and Distributed Programming in Java

Nível intermediário

Aprox. 18 horas para completar

Inglês

Sua empresa se beneficiaria do treinamento dos funcionários em habilidades sob demanda?

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Instrutores

Classificação do instrutor

4,75/5 (44 classificações)

Vivek Sarkar

Professor

Department of Computer Science

53.090 aprendizes

3 Cursos

oferecido por

Universidade Rice

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Programa - O que você aprenderá com este curso

Classificação do conteúdo94%(1,549 classificações)

Semana

Semana 1

1 hora para concluir

Welcome to the Course!

1 hora para concluir

1 vídeo (Total 1 mín.), 5 leituras, 1 teste

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.

4 horas para concluir

6 vídeos (Total 49 mín.), 6 leituras, 2 testes

Semana

Semana 2

4 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.

4 horas para concluir

6 vídeos (Total 43 mín.), 6 leituras, 2 testes

6 videos

2.1 Introduction to Sockets7min

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

15 minutos para concluir

2 vídeos (Total 13 mín.), 1 leitura

Semana

Semana 3

4 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.

4 horas para concluir

6 vídeos (Total 49 mín.), 6 leituras, 2 testes

6 videos

3.1 Single Program Multiple Data (SPMD) model7min

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

Semana 4

4 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.

4 horas para concluir

6 vídeos (Total 44 mín.), 7 leituras, 2 testes

6 videos

4.1 Processes and Threads9min

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"

20 minutos para concluir

2 vídeos (Total 10 mín.), 1 leitura

Avaliações

4.6

77 avaliações

5 stars
69,24%
4 stars
22,80%
3 stars
5,02%
2 stars
0,83%
1 star
2,09%

Principais avaliações do DISTRIBUTED PROGRAMMING IN JAVA

por SM22 de set de 2017

The course was well laid out and introduced me to some new concepts.

por AP23 de abr de 2020

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

por RS28 de jun de 2019

Amazing Course, was able to learn new things and had the best experience

por SA27 de abr de 2020

A very good course, I learnt a lot from it, thank you Coursera.

Visualizar todas as avaliações

Sobre 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).

Parallel, Concurrent, and Distributed Programming in Java

Perguntas Frequentes – FAQ

Quando terei acesso às palestras e às tarefas?
O acesso a palestras e tarefas depende do tipo de inscrição. Se você participar de um curso como ouvinte, você poderá ver quase todo o conteúdo do curso gratuitamente. Para acessar tarefas valendo nota e obter um Certificado, você precisará adquirir a experiência do Certificado, durante ou após a participação como ouvinte. Se você não vir a opção de participar como ouvinte:
o curso pode não oferecer essa opção. Você pode experimentar um teste gratuito ou solicitar o auxílio financeiro.
Em vez disso, o curso pode oferecer 'Curso completo, sem Certificado'. Com esta opção, é possível ver todo o conteúdo do curso, enviar as avaliações necessárias e obter uma nota final. Isso também significa que você não poderá comprar uma experiência de Certificado.
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.
Existe algum auxílio financeiro disponível?
Sim. Em programas de aprendizado selecionados, você pode solicitar auxílio financeiro ou uma bolsa de estudos caso não consiga pagar a taxa de inscrição. Se o auxílio financeiro ou bolsa de estudos estiver disponível para a seleção do seu programa de aprendizado, você encontrará um link para se inscrever na página de descrição.
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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