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.
이 강좌는 Parallel, Concurrent, and Distributed Programming in Java 특화 과정의 일부입니다.
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다음 특화 과정의 3개 강좌 중 1번째 강좌:
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자막: 아랍어, 프랑스어, 포르투갈어 (유럽), 이탈리아어, 베트남어, 독일어, 러시아어, 영어, 스페인어
귀하가 습득할 기술
- Dataflow
- Parallel Computing
- Java Concurrency
- Data Parallelism
유연한 마감일
일정에 따라 마감일을 재설정합니다.
공유 가능한 수료증
완료 시 수료증 획득
100% 온라인
지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.
다음 특화 과정의 3개 강좌 중 1번째 강좌:
중급 단계
완료하는 데 약 19시간 필요
영어
자막: 아랍어, 프랑스어, 포르투갈어 (유럽), 이탈리아어, 베트남어, 독일어, 러시아어, 영어, 스페인어
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라이스 대학교
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.
강의 계획표 - 이 강좌에서 배울 내용
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
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
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
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
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
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
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
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- 3 stars6.48%
- 2 stars1.05%
- 1 star0.43%
PARALLEL PROGRAMMING IN JAVA의 최상위 리뷰
BK 제공2020년 7월 19일
I have never known these things existed and changed my perspective how to take advantage of multiple processor. I have used numpy in python but now I know how these are fast.
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.
YY 제공2019년 2월 27일
This course explain the concepts in a great way but the exercises needs to be more rich with problems to enhance our understanding to the concepts.
MB 제공2018년 1월 27일
Instructor is awesome. However instead of sharing ready libraries for parallelism (like forall loop) in section 3, native codes should be used for forall loops etc.
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.
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