À propos de ce cours
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Niveau intermédiaire

Approx. 15 heures pour terminer

Recommandé : Four weeks of study, 4-8 hours/week depending on past experience with sequential programming in Java...


Sous-titres : Anglais

Compétences que vous acquerrez

DataflowParallel ComputingJava ConcurrencyData Parallelism

100 % en ligne

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.

Dates limites flexibles

Réinitialisez les dates limites selon votre disponibilité.

Niveau intermédiaire

Approx. 15 heures pour terminer

Recommandé : Four weeks of study, 4-8 hours/week depending on past experience with sequential programming in Java...


Sous-titres : Anglais

Programme du cours : ce que vous apprendrez dans ce cours

1 heure pour terminer

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 vidéo (Total 1 min), 5 lectures, 1 quiz
1 vidéo
5 lectures
General Course Info5 min
Course Icon Legend5 min
Discussion Forum Guidelines5 min
Pre-Course Survey10 min
Mini Project 0: Setup10 min
4 heures pour terminer

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 vidéos (Total 42 min), 6 lectures, 2 quiz
7 vidéos
1.4 Multiprocessor Scheduling, Parallel Speedup8 min
1.5 Amdahl's Law5 min
ReciprocalArraySum using Async-Finish (Demo)4 min
ReciprocalArraySum using RecursiveAction's in Java's Fork/Join Framework (Demo)5 min
6 lectures
1.1 Lecture Summary5 min
1.2 Lecture Summary5 min
1.3 Lecture Summary5 min
1.4 Lecture Summary5 min
1.5 Lecture Summary5 min
Mini Project 1: Reciprocal-Array-Sum using the Java Fork/Join Framework10 min
1 exercice pour s'entraîner
Module 1 Quiz30 min
4 heures pour terminer

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 vidéos (Total 40 min), 6 lectures, 2 quiz
7 vidéos
2.4 Java Streams5 min
2.5 Data Races and Determinism9 min
ReciprocalArraySum using RecursiveTask's in Java's Fork/Join Framework (Demo)3 min
Parallel List Processing Using Java Streams (Demo)4 min
6 lectures
2.1 Lecture Summary10 min
2.2 Lecture Summary10 min
2.3 Lecture Summary10 min
2.4 Lecture Summary10 min
2.5 Lecture Summary10 min
Mini Project 2: Analyzing Student Statistics Using Java Parallel Streams10 min
1 exercice pour s'entraîner
Module 2 Quiz30 min
23 minutes pour terminer

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 vidéos (Total 13 min), 1 lecture
4 heures pour terminer

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 vidéos (Total 41 min), 6 lectures, 2 quiz
7 vidéos
3.4 Parallel One-Dimensional Iterative Averaging8 min
3.5 Iteration Grouping/Chunking in Parallel Loops6 min
Parallel Matrix Multiplication (Demo)4 min
Parallel One-Dimensional Iterative Averaging (Demo)5 min
6 lectures
3.1 Lecture Summary10 min
3.2 Lecture Summary10 min
3.3 Lecture Summary10 min
3.4 Lecture Summary10 min
3.5 Lecture Summary10 min
Mini Project 3: Parallelizing Matrix-Matrix Multiply Using Loop Parallelism10 min
1 exercice pour s'entraîner
Module 3 Quiz30 min
4 heures pour terminer

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 vidéos (Total 38 min), 7 lectures, 2 quiz
7 vidéos
4.4 Pipeline Parallelism5 min
4.5 Data Flow Parallelism5 min
Phaser Examples6 min
Pipeline & Data Flow Parallelism7 min
7 lectures
4.1 Lecture Summary10 min
4.2 Lecture Summary10 min
4.3 Lecture Summary10 min
4.4 Lecture Summary10 min
4.5 Lecture Summary10 min
Mini Project 4: Using Phasers to Optimize Data-Parallel Applications10 min
Exit Survey10 min
1 exercice pour s'entraîner
Module 4 Quiz30 min
20 minutes pour terminer

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 vidéos (Total 10 min), 1 lecture
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Principaux examens pour Parallel Programming in Java

par LGDec 13th 2017

This is a great course in parallel programming. The videos were very clear, summaries reinforced the video material and the programming projects and quizzes were challenging but not overwhelming.

par SVAug 28th 2017

Great course. Introduces Parallel Programming in Java in a gentle way.\n\nKudos to Professor Vivek Sarkar for simplifying complex concepts and presenting them in an elegant manner.



Vivek Sarkar

Department of Computer Science

À propos de Université de 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....

À propos de la Spécialisation 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

Foire Aux Questions

  • Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.

  • Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.

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