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.
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DataflowParallel ComputingJava ConcurrencyData Parallelism
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일정에 따라 마감일을 재설정합니다.
중급 단계
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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.
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완료하는 데 1시간 필요
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.
완료하는 데 1시간 필요
1개 동영상 (총 1분), 5 개의 읽기 자료, 1 개의 테스트
1개의 동영상
5개의 읽기 자료
General Course Info5m
Course Icon Legend5m
Discussion Forum Guidelines5m
Pre-Course Survey10m
Mini Project 0: Setup10m
완료하는 데 4시간 필요
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.
완료하는 데 4시간 필요
7개 동영상 (총 42분), 6 개의 읽기 자료, 2 개의 테스트
7개의 동영상
1.2 Tasks in Java's Fork/Join Framework5m
1.3 Computation Graphs, Work, Span7m
1.4 Multiprocessor Scheduling, Parallel Speedup8m
1.5 Amdahl's Law5m
ReciprocalArraySum using Async-Finish (Demo)4m
ReciprocalArraySum using RecursiveAction's in Java's Fork/Join Framework (Demo)5m
6개의 읽기 자료
1.1 Lecture Summary5m
1.2 Lecture Summary5m
1.3 Lecture Summary5m
1.4 Lecture Summary5m
1.5 Lecture Summary5m
Mini Project 1: Reciprocal-Array-Sum using the Java Fork/Join Framework10m
1개 연습문제
Module 1 Quiz30m
완료하는 데 4시간 필요
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.
완료하는 데 4시간 필요
7개 동영상 (총 40분), 6 개의 읽기 자료, 2 개의 테스트
7개의 동영상
2.2 Futures in Java's Fork/Join Framework5m
2.3 Memoization6m
2.4 Java Streams5m
2.5 Data Races and Determinism9m
ReciprocalArraySum using RecursiveTask's in Java's Fork/Join Framework (Demo)3m
Parallel List Processing Using Java Streams (Demo)4m
6개의 읽기 자료
2.1 Lecture Summary10m
2.2 Lecture Summary10m
2.3 Lecture Summary10m
2.4 Lecture Summary10m
2.5 Lecture Summary10m
Mini Project 2: Analyzing Student Statistics Using Java Parallel Streams10m
1개 연습문제
Module 2 Quiz30m
완료하는 데 23분 필요
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.
완료하는 데 23분 필요
2개 동영상 (총 13분), 1 개의 읽기 자료
2개의 동영상
Industry Professionals on Parallelism - Jake Kornblau and Margaret Kelley, Software Engineers6m
1개의 읽기 자료
About these Talks10m
완료하는 데 4시간 필요
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.
완료하는 데 4시간 필요
7개 동영상 (총 41분), 6 개의 읽기 자료, 2 개의 테스트
7개의 동영상
3.2 Parallel Matrix Multiplication4m
3.3 Barriers in Parallel Loops5m
3.4 Parallel One-Dimensional Iterative Averaging8m
3.5 Iteration Grouping/Chunking in Parallel Loops6m
Parallel Matrix Multiplication (Demo)4m
Parallel One-Dimensional Iterative Averaging (Demo)5m
6개의 읽기 자료
3.1 Lecture Summary10m
3.2 Lecture Summary10m
3.3 Lecture Summary10m
3.4 Lecture Summary10m
3.5 Lecture Summary10m
Mini Project 3: Parallelizing Matrix-Matrix Multiply Using Loop Parallelism10m
1개 연습문제
Module 3 Quiz30m
완료하는 데 5시간 필요
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.
완료하는 데 5시간 필요
7개 동영상 (총 38분), 7 개의 읽기 자료, 2 개의 테스트
7개의 동영상
4.2 Point-to-Point Sychronization with Phasers4m
4.3 One-Dimensional Iterative Averaging with Phasers4m
4.4 Pipeline Parallelism5m
4.5 Data Flow Parallelism5m
Phaser Examples6m
Pipeline & Data Flow Parallelism7m
7개의 읽기 자료
4.1 Lecture Summary10m
4.2 Lecture Summary10m
4.3 Lecture Summary10m
4.4 Lecture Summary10m
4.5 Lecture Summary10m
Mini Project 4: Using Phasers to Optimize Data-Parallel Applications10m
Exit Survey10m
1개 연습문제
Module 4 Quiz30m
완료하는 데 20분 필요
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.
완료하는 데 20분 필요
2개 동영상 (총 10분), 1 개의 읽기 자료
2개의 동영상
Industry Professional on Distribution - Dr. Eric Allen, Senior Vice President, Two Sigma6m
1개의 읽기 자료
Our Other Course Offerings10m
검토
PARALLEL PROGRAMMING IN JAVA의 최상위 리뷰
JC 제공2020년 6월 25일
Great introduction to parallel programming. Lectures were clear, summaries were helpful, quizzes were not trivial, discussion forum is good, but the assignments' grading system could be improved.
SD 제공2020년 5월 25일
I found the explanation of how to precisely code phasors lacking as the Thread stuff was already provided in sample code and never explained. Quite satisfied with the rest of the course though.
KK 제공2018년 6월 18일
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.
VZ 제공2017년 11월 6일
Very useful course about parallel programming theory and practice. Could be less examples with custom libraries instead of standard java features looking to practical usage at work.
Parallel, Concurrent, and Distributed Programming in Java 특화 과정 정보
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).
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