What Is Programming? And How To Get Started
January 28, 2025
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This course is part of Functional Programming in Scala Specialization
Instructors: Prof. Viktor Kuncak
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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.
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.
9 videos5 readings3 programming assignments
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.
6 videos2 programming assignments
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.
5 videos2 programming assignments
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.
5 videos2 programming assignments
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
Instructor ratings
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
Korea Advanced Institute of Science and Technology(KAIST)
Course
Korea Advanced Institute of Science and Technology(KAIST)
Course
Duke University
Specialization
LearnKartS
Course
1,841 reviews
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Reviewed on May 16, 2017
The assignment could be optimized and avoid to be more academic since it may make student lost focus and spend too much time on the question itself rather than the parallel programming
Reviewed on Mar 31, 2017
Its a very good course! perhaps the in the practice code, before jumping into the problems they can provide a couple of simple examples/questions such that the main ideas are learnt.
Reviewed on Dec 10, 2016
Good course but scala understanding is required for this course. So please register for prior course in the certification task to easily complete this course.
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