Informações sobre o curso: This course teaches learners (industry professionals and students) the fundamental concepts of parallel programming in the context of Java 8. Parallel programming enables developers to use multicore computers to make their applications run faster by using multiple processors at the same time. By the end of this course, you will learn how to use popular parallel Java frameworks (such as ForkJoin, Stream, and Phaser) to write parallel programs for a wide range of multicore platforms including servers, desktops, or mobile devices, while also learning about their theoretical foundations including computation graphs, ideal parallelism, parallel speedup, Amdahl's Law, data races, and determinism. Why take this course? • All computers are multicore computers, so it is important for you to learn how to extend your knowledge of sequential Java programming to multicore parallelism. • Java 7 and Java 8 have introduced new frameworks for parallelism (ForkJoin, Stream) that have significantly changed the paradigms for parallel programming since the early days of Java. • 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: • Theory of parallelism: computation graphs, work, span, ideal parallelism, parallel speedup, Amdahl's Law, data races, and determinism • Task parallelism using Java’s ForkJoin framework • Functional parallelism using Java’s Future and Stream frameworks • Loop-level parallelism with extensions for barriers and iteration grouping (chunking) • Dataflow parallelism using the Phaser framework and data-driven tasks Mastery of these concepts will enable you to immediately apply them in the context of multicore Java programs, and will also provide the foundation for mastering other parallel programming systems that you may encounter in the future (e.g., C++11, OpenMP, .Net Task Parallel Library).

Para quem é direcionado este curso: The course is targeted at an audience that is already familiar with sequential programming in Java, including a basic knowledge of Java 8 lambdas. In addition, we assume that each student has access to a laptop/desktop computer with a recent installation of Java 8. The course site includes instructions on how to obtain this installation, if needed.

Desenvolvido por: Rice University

Ministrado por: Vivek Sarkar, Professor
Department of Computer Science

Informações básicas	Curso 1 de 3 no Parallel, Concurrent, and Distributed Programming in Java Specialization
Nível	Intermediário
Compromisso	Four weeks of study, 4-8 hours/week depending on past experience with sequential programming in Java
Idioma	English
Requisitos de hardware	Access to a computer that can run Java 8 programs for optional course projects
Como ser aprovado	Seja aprovado em todas as tarefas para concluir o curso.
Classificação do usuário	4.5 estrelas Classificação média do usuário 4.5Veja o que os aprendizes disseram

Programa

SEMANA 1

Welcome to the Course!

Welcome to Parallel 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 vídeos, 5 leituras

Reading: General Course Info
Reading: Course Icon Legend
Reading: Discussion Forum Guidelines
Discussion Prompt: Get to know your classmates!
Reading: Pre-Course Survey
Reading: Mini Project 0: Setup
Ungraded Programming: Mini Project 0 Submission

Task Parallelism

In this module, we will learn the fundamentals of task parallelism. Tasks are the most basic unit of parallel programming. An increasing number of programming languages (including Java and C++) are moving from older thread-based approaches to more modern task-based approaches for parallel programming. We will learn about task creation, task termination, and the “computation graph” theoretical model for understanding various properties of task-parallel programs. These properties include work, span, ideal parallelism, parallel speedup, and Amdahl’s Law. We will also learn popular Java APIs for task parallelism, most notably the Fork/Join framework.

7 videos, 6 leituras

Reading: 1.1 Lecture Summary
Vídeo: 1.2 Tasks in Java's Fork/Join Framework
Reading: 1.2 Lecture Summary
Vídeo: 1.3 Computation Graphs, Work, Span
Reading: 1.3 Lecture Summary
Vídeo: 1.4 Multiprocessor Scheduling, Parallel Speedup
Reading: 1.4 Lecture Summary
Vídeo: 1.5 Amdahl's Law
Reading: 1.5 Lecture Summary
Vídeo: ReciprocalArraySum using Async-Finish (Demo)
Vídeo: ReciprocalArraySum using RecursiveAction's in Java's Fork/Join Framework (Demo)
Reading: Mini Project 1: Reciprocal-Array-Sum using the Java Fork/Join Framework

Nota atribuída: Mini Project 1 Submission

Nota atribuída: Module 1 Quiz

SEMANA 2

Functional Parallelism

Welcome to Module 2! In this module, we will learn about approaches to parallelism that have been inspired by functional programming. Advocates of parallel functional programming have argued for decades that functional parallelism can eliminate many hard-to-detect bugs that can occur with imperative parallelism. We will learn about futures, memoization, and streams, as well as data races, a notorious class of bugs that can be avoided with functional parallelism. We will also learn Java APIs for functional parallelism, including the Fork/Join framework and the Stream API’s.

7 videos, 6 leituras

Reading: 2.1 Lecture Summary
Vídeo: 2.2 Futures in Java's Fork/Join Framework
Reading: 2.2 Lecture Summary
Vídeo: 2.3 Memoization
Reading: 2.3 Lecture Summary
Vídeo: 2.4 Java Streams
Reading: 2.4 Lecture Summary
Vídeo: 2.5 Data Races and Determinism
Reading: 2.5 Lecture Summary
Vídeo: ReciprocalArraySum using RecursiveTask's in Java's Fork/Join Framework (Demo)
Vídeo: Parallel List Processing Using Java Streams (Demo)
Reading: Mini Project 2: Analyzing Student Statistics Using Java Parallel Streams

Nota atribuída: Mini Project 2 Submission

Nota atribuída: Module 2 Quiz

Talking to Two Sigma: Using it in the Field

Join Professor Vivek Sarkar as he talks with Two Sigma Managing Director, Jim Ward, and Software Engineers, Margaret Kelley and Jake Kornblau, at their downtown Houston, Texas office about the importance of parallel programming.

2 videos, 1 leituras

SEMANA 3

Loop Parallelism

Welcome to Module 3, and congratulations on reaching the midpoint of this course! It is well known that many applications spend a majority of their execution time in loops, so there is a strong motivation to learn how loops can be sped up through the use of parallelism, which is the focus of this module. We will start by learning how parallel counted-for loops can be conveniently expressed using forall and stream APIs in Java, and how these APIs can be used to parallelize a simple matrix multiplication program. We will also learn about the barrier construct for parallel loops, and illustrate its use with a simple iterative averaging program example. Finally, we will learn the importance of grouping/chunking parallel iterations to reduce overhead.

7 videos, 6 leituras

Reading: 3.1 Lecture Summary
Vídeo: 3.2 Parallel Matrix Multiplication
Reading: 3.2 Lecture Summary
Vídeo: 3.3 Barriers in Parallel Loops
Reading: 3.3 Lecture Summary
Vídeo: 3.4 Parallel One-Dimensional Iterative Averaging
Reading: 3.4 Lecture Summary
Vídeo: 3.5 Iteration Grouping/Chunking in Parallel Loops
Reading: 3.5 Lecture Summary
Vídeo: Parallel Matrix Multiplication (Demo)
Vídeo: Parallel One-Dimensional Iterative Averaging (Demo)
Reading: Mini Project 3: Parallelizing Matrix-Matrix Multiply Using Loop Parallelism

Nota atribuída: Mini Project 3 Submission

Nota atribuída: Module 3 Quiz

SEMANA 4

Data flow Synchronization and Pipelining

Welcome to the last module of the course! In this module, we will wrap up our introduction to parallel programming by learning how data flow principles can be used to increase the amount of parallelism in a program. We will learn how Java’s Phaser API can be used to implement “fuzzy” barriers, and also “point-to-point” synchronizations as an optimization of regular barriers by revisiting the iterative averaging example. Finally, we will also learn how pipeline parallelism and data flow models can be expressed using Java APIs.

7 videos, 7 leituras

Reading: 4.1 Lecture Summary
Vídeo: 4.2 Point-to-Point Sychronization with Phasers
Reading: 4.2 Lecture Summary
Vídeo: 4.3 One-Dimensional Iterative Averaging with Phasers
Reading: 4.3 Lecture Summary
Vídeo: 4.4 Pipeline Parallelism
Reading: 4.4 Lecture Summary
Vídeo: 4.5 Data Flow Parallelism
Reading: 4.5 Lecture Summary
Vídeo: Phaser Examples
Vídeo: Pipeline & Data Flow Parallelism
Reading: Mini Project 4: Using Phasers to Optimize Data-Parallel Applications
Reading: Exit Survey

Nota atribuída: Mini Project 4 Submission

Nota atribuída: Module 4 Quiz

Continue Your Journey with the Specialization "Parallel, Concurrent, and Distributed Programming in Java"

The next two videos will showcase the importance of learning about Concurrent Programming and Distributed 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 videos, 1 leituras

Reading: Our Other Course Offerings
Vídeo: Industry Professional on Distribution - Dr. Eric Allen, Senior Vice President, Two Sigma

Perguntas frequentes

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Coursework

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Desenvolvedores

Rice University

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.

Custo

	Comprar curso
Acesso aos materiais do curso	Disponível
Acesso a materiais valendo nota	Disponível
Receba uma nota final	Disponível
Obtenha um Certificado de Curso compartilhável	Disponível

Classificações e avaliações

Avaliado em 4.5 de 5 decorrente de 347 avaliações

the lecturer should wear black shirt to make the video more readable.

Fantastic introduction to parallelism in Java. Starts basic enough for those with zero experience with parallel computing, goes far enough to give more experienced Java devs some things to chew on, gives practical examples for how to apply parallel techniques throughout. Highly recommended!

Excellent Course.I always wanted a good course on java concurrency and parallel programming.And finish->async, isolated, forAsync constructs are awesome.I have learnt much from this course.