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?
ā¢ 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.
ā¢ 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.
ā¢ 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.
ā¢ 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:
ā¢ Theory of parallelism: computation graphs, work, span, ideal parallelism, parallel speedup, Amdahl's Law, data races, and determinism
ā¢ Task parallelism using Javaās ForkJoin framework
ā¢ Functional parallelism using Javaās Future and Stream frameworks
ā¢ Loop-level parallelism with extensions for barriers and iteration grouping (chunking)
ā¢ 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).
Este curso forma parte de Programa Especializado - Parallel, Concurrent, and Distributed Programming in Java
Parallel Programming in Java
ofrecido por
Acerca de este Curso
4.6
596 calificaciones
Habilidades que obtendrƔs
DataflowParallel ComputingJava ConcurrencyData Parallelism
Programa - QuƩ aprenderƔs en este curso
Semana
11 hora para completar
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 video (Total 1 minutos), 5 readings, 1 quiz
1 video
5 lecturas
General Course Info5m
Course Icon Legend5m
Discussion Forum Guidelines5m
Pre-Course Survey10m
Mini Project 0: Setup10m
4 horas para completar
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 ācomputation graphā theoretical model for understanding various properties of task-parallel programs. These properties include work, span, ideal parallelism, parallel speedup, and Amdahlās Law. We will also learn popular Java APIs for task parallelism, most notably the Fork/Join framework.
7 videos (Total 42 minutos), 6 readings, 2 quizzes
7 videos
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
6 lecturas
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
1 ejercicio de prƔctica
Module 1 Quiz30m
Semana
24 horas para completar
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 APIās.
7 videos (Total 40 minutos), 6 readings, 2 quizzes
7 videos
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
6 lecturas
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
1 ejercicio de prƔctica
Module 2 Quiz30m
23 minutos para completar
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 (Total 13 minutos), 1 reading
2 videos
Industry Professionals on Parallelism - Jake Kornblau and Margaret Kelley, Software Engineers6m
1 lectura
About these Talks10m
Semana
34 horas para completar
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 (Total 41 minutos), 6 readings, 2 quizzes
7 videos
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
6 lecturas
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
1 ejercicio de prƔctica
Module 3 Quiz30m
Semana
44 horas para completar
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 Javaās Phaser API can be used to implement āfuzzyā barriers, and also āpoint-to-pointā 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 (Total 38 minutos), 7 readings, 2 quizzes
7 videos
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
7 lecturas
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
1 ejercicio de prƔctica
Module 4 Quiz30m
20 minutos para completar
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 (Total 10 minutos), 1 reading
2 videos
Industry Professional on Distribution - Dr. Eric Allen, Senior Vice President, Two Sigma6m
1 lectura
Our Other Course Offerings10m
25%
comenzĆ³ una nueva carrera despuĆ©s de completar estos cursos
12%
consiguiĆ³ un beneficio tangible en su carrera profesional gracias a este curso
Principales revisiones
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.
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.
Instructor
Vivek Sarkar
ProfessorDepartment of Computer Science
Acerca de Universidad 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.
Acerca del programa especializado 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).
Preguntas Frecuentes
ĀæCuĆ”ndo podrĆ© acceder a las lecciones y tareas?
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ĀæQuĆ© recibirĆ© si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darĆ”n un Certificado cuando completes el trabajo. Se aƱadirĆ” tu Certificado electrĆ³nico a la pĆ”gina Logros. Desde allĆ, puedes imprimir tu Certificado o aƱadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
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