With every smartphone and computer now boasting multiple processors, the use of functional ideas to facilitate parallel programming is becoming increasingly widespread. In this course, you'll learn the fundamentals of parallel programming, from task parallelism to data parallelism. In particular, you'll see how many familiar ideas from functional programming map perfectly to to the data parallel paradigm. We'll start the nuts and bolts how to effectively parallelize familiar collections operations, and we'll build up to parallel collections, a production-ready data parallel collections library available in the Scala standard library. Throughout, we'll apply these concepts through several hands-on examples that analyze real-world data, such as popular algorithms like k-means clustering. Learning Outcomes. By the end of this course you will be able to: - reason about task and data parallel programs, - express common algorithms in a functional style and solve them in parallel, - competently microbenchmark parallel code, - write programs that effectively use parallel collections to achieve performance Recommended background: You should have at least one year programming experience. Proficiency with Java or C# is ideal, but experience with other languages such as C/C++, Python, Javascript or Ruby is also sufficient. You should have some familiarity using the command line. This course is intended to be taken after Functional Program Design in Scala: https://www.coursera.org/learn/progfun2.
This course is part of the Functional Programming in Scala Specialization
Parallel programming
Offered By
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Skills you will gain
Learner Career Outcomes
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100% online
Course 3 of 5 in the
Flexible deadlines
Intermediate Level
Approx. 16 hours to complete
English
Syllabus - What you will learn from this course
Parallel Programming
We motivate parallel programming and introduce the basic constructs for building parallel programs on JVM and Scala. Examples such as array norm and Monte Carlo computations illustrate these concepts. We show how to estimate work and depth of parallel programs as well as how to benchmark the implementations.
Basic Task Parallel Algorithms
We continue with examples of parallel algorithms by presenting a parallel merge sort. We then explain how operations such as map, reduce, and scan can be computed in parallel. We present associativity as the key condition enabling parallel implementation of reduce and scan.
Data-Parallelism
We show how data parallel operations enable the development of elegant data-parallel code in Scala. We give an overview of the parallel collections hierarchy, including the traits of splitters and combiners that complement iterators and builders from the sequential case.
Data Structures for Parallel Computing
We give a glimpse of the internals of data structures for parallel computing, which helps us understand what is happening under the hood of parallel collections.
Top reviews from Parallel programming
The course is fairly advanced and you would need to review the materials many times to understand the concept. The assignments are definitely fun and not as straightforward as other courses.
Superb study material. Learnt a lot during this course. I am not much into mathematical stuff, but got a hang of how to break problems and improve efficiency through parallelism.
Instructors
Prof. Viktor Kuncak
Associate Professor
Dr. Aleksandar Prokopec
Principal ResearcherAbout École Polytechnique Fédérale de Lausanne
About the Functional Programming in Scala Specialization
Frequently Asked Questions
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