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中級

完成時間大約為15 小時

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

英語(English)

字幕:英語(English)

您將獲得的技能

DataflowParallel ComputingJava ConcurrencyData Parallelism

100% 在線

立即開始,按照自己的計劃學習。

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

完成時間大約為15 小時

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

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

1
完成時間為 1 小時

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.

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1 個視頻 (總計 1 分鐘), 5 個閱讀材料, 1 個測驗
1 個視頻
5 個閱讀材料
General Course Info5分鐘
Course Icon Legend5分鐘
Discussion Forum Guidelines5分鐘
Pre-Course Survey10分鐘
Mini Project 0: Setup10分鐘
完成時間為 4 小時

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.

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7 個視頻 (總計 42 分鐘), 6 個閱讀材料, 2 個測驗
7 個視頻
1.4 Multiprocessor Scheduling, Parallel Speedup8分鐘
1.5 Amdahl's Law5分鐘
ReciprocalArraySum using Async-Finish (Demo)4分鐘
ReciprocalArraySum using RecursiveAction's in Java's Fork/Join Framework (Demo)5分鐘
6 個閱讀材料
1.1 Lecture Summary5分鐘
1.2 Lecture Summary5分鐘
1.3 Lecture Summary5分鐘
1.4 Lecture Summary5分鐘
1.5 Lecture Summary5分鐘
Mini Project 1: Reciprocal-Array-Sum using the Java Fork/Join Framework10分鐘
1 個練習
Module 1 Quiz30分鐘
2
完成時間為 4 小時

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.

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7 個視頻 (總計 40 分鐘), 6 個閱讀材料, 2 個測驗
7 個視頻
2.4 Java Streams5分鐘
2.5 Data Races and Determinism9分鐘
ReciprocalArraySum using RecursiveTask's in Java's Fork/Join Framework (Demo)3分鐘
Parallel List Processing Using Java Streams (Demo)4分鐘
6 個閱讀材料
2.1 Lecture Summary10分鐘
2.2 Lecture Summary10分鐘
2.3 Lecture Summary10分鐘
2.4 Lecture Summary10分鐘
2.5 Lecture Summary10分鐘
Mini Project 2: Analyzing Student Statistics Using Java Parallel Streams10分鐘
1 個練習
Module 2 Quiz30分鐘
完成時間為 23 分鐘

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.

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2 個視頻 (總計 13 分鐘), 1 個閱讀材料
3
完成時間為 4 小時

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.

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7 個視頻 (總計 41 分鐘), 6 個閱讀材料, 2 個測驗
7 個視頻
3.4 Parallel One-Dimensional Iterative Averaging8分鐘
3.5 Iteration Grouping/Chunking in Parallel Loops6分鐘
Parallel Matrix Multiplication (Demo)4分鐘
Parallel One-Dimensional Iterative Averaging (Demo)5分鐘
6 個閱讀材料
3.1 Lecture Summary10分鐘
3.2 Lecture Summary10分鐘
3.3 Lecture Summary10分鐘
3.4 Lecture Summary10分鐘
3.5 Lecture Summary10分鐘
Mini Project 3: Parallelizing Matrix-Matrix Multiply Using Loop Parallelism10分鐘
1 個練習
Module 3 Quiz30分鐘
4
完成時間為 5 小時

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.

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7 個視頻 (總計 38 分鐘), 7 個閱讀材料, 2 個測驗
7 個視頻
4.4 Pipeline Parallelism5分鐘
4.5 Data Flow Parallelism5分鐘
Phaser Examples6分鐘
Pipeline & Data Flow Parallelism7分鐘
7 個閱讀材料
4.1 Lecture Summary10分鐘
4.2 Lecture Summary10分鐘
4.3 Lecture Summary10分鐘
4.4 Lecture Summary10分鐘
4.5 Lecture Summary10分鐘
Mini Project 4: Using Phasers to Optimize Data-Parallel Applications10分鐘
Exit Survey10分鐘
1 個練習
Module 4 Quiz30分鐘
完成時間為 20 分鐘

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.

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2 個視頻 (總計 10 分鐘), 1 個閱讀材料
4.6
126 個審閱Chevron Right

25%

完成這些課程後已開始新的職業生涯

12%

通過此課程獲得實實在在的工作福利

來自Parallel Programming in Java的熱門評論

創建者 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.

創建者 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.

講師

Avatar

Vivek Sarkar

Professor
Department of Computer Science

關於 莱斯大学

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....

關於 Parallel, Concurrent, and Distributed Programming in Java 專項課程

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

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