About this course: 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.
Taught by: Prof. Viktor Kuncak, Associate Professor
School of Computer and Communication Sciences
Taught by: Dr. Aleksandar Prokopec, Principal Researcher
Oracle Labs
| Basic Info | Course 3 of 5 in the Functional Programming in Scala Specialization |
| Level | Intermediate |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
- Video: Course Overview
- Video: Introduction to Parallel Computing
- Video: Parallelism on the JVM I
- Video: Parallelism on the JVM II
- Video: Running Computations in Parallel
- Video: Monte Carlo Method to Estimate Pi
- Video: First-Class Tasks
- Video: How Fast are Parallel Programs?
- Video: Benchmarking Parallel Programs
- Reading: Tools Setup
- Reading: Eclipse Tutorial
- Reading: IntelliJ IDEA Tutorial
- Reading: Sbt Tutorial
- Reading: Submitting Solutions
- Ungraded Programming: Example
- Ungraded Programming: Parallel Box Blur Filter
- Video: Parallel Sorting
- Video: Data Operations and Parallel Mapping
- Video: Parallel Fold (Reduce) Operation
- Video: Associativity I
- Video: Associativity II
- Video: Parallel Scan (Prefix Sum) Operation
- Ungraded Programming: Reductions and Prefix Sums
- Video: Data-Parallel Programming
- Video: Data-Parallel Operations I
- Video: Data-Parallel Operations II
- Video: Scala Parallel Collections
- Video: Splitters and Combiners
- Ungraded Programming: K-Means
- Video: Implementing Combiners
- Video: Parallel Two-phase Construction
- Video: Conc-tree Data Structure
- Video: Amortized, Constant-time Append Operation
- Video: Conc-Tree Combiners
- Ungraded Programming: Barnes-Hut Simulation
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|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
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Course with excellent content, methodology and teacher. It was an extraordinary learning experience
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.
Great course -- I learned a lot.
Parts of it were very heavy too, and not all of the lecture material was examined.
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I liked it a lot!