Este curso forma parte de Programa especializado: Parallel, Concurrent, and Distributed Programming in Java

4.6
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477 calificaciones

94 %

Vivek Sarkar

21.104 ya inscrito

Ofrecido Por

Programa especializado: Parallel, Concurrent, and Distributed Programming in JavaUniversidad Rice

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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++).

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Curso 3 de 3 en

Programa especializado: Parallel, Concurrent, and Distributed Programming in Java

Nivel intermedio

Aprox. 18 horas para completar

Inglés (English)

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Habilidades que obtendrás

Distributed Computing
Actor Model
Parallel Computing
Reactive Programming

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Curso 3 de 3 en

Programa especializado: Parallel, Concurrent, and Distributed Programming in Java

Nivel intermedio

Aprox. 18 horas para completar

Inglés (English)

¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?

Prueba Coursera para negocios

Instructor

Calificación del instructor

4,75/5 (44 calificaciones)

Vivek Sarkar

Professor

Department of Computer Science

53.176 alumnos

3 cursos

Ofrecido por

Universidad Rice

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Programa - Qué aprenderás en este curso

Calificación del contenido94%(1,551 calificaciones)

Semana

Semana 1

1 hora para completar

Welcome to the Course!

1 hora para completar

1 video (Total 1 minutos), 5 lecturas, 1 cuestionario

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

Semana

Semana 2

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

15 minutos para completar

Talking to Two Sigma: Using it in the Field

15 minutos para completar

2 videos (Total 13 minutos), 1 lectura

Semana

Semana 3

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

3.1 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 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

Semana

Semana 4

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

4.1 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 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"

20 minutos para completar

2 videos (Total 10 minutos), 1 lectura

Reseñas

4.6

77 reseña

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

Principales reseñas sobre DISTRIBUTED PROGRAMMING IN JAVA

por BM30 de oct. de 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.

por TM4 de may. de 2018

First two courses are better. This one is quick and a little vague

por JC4 de jul. de 2020

Great lectures. I really learned a lot about distributed computing.

por AP23 de abr. de 2020

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

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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.

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

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.
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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