Course
Parallel Programming in R
IntermediateSkill Level
Updated 06/2024RProgramming4 hr16 videos49 Exercises3,950 XPStatement of Accomplishment
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Speed Up Your Code with Parallel Programming
R programming language is a key part of the modern tech stack. But sometimes, R code takes a long time to run. The good news is that most modern computers have multiple processors. This course on parallel programming can help you speed up your code by harnessing the hardware you already have.
Learn the Key Concepts
In this course, you will systematically learn the key concepts of parallel programming. You will profile and benchmark common computations like bootstraps and function mappings. You will also learn to identify operations that can benefit from parallelization.
Use R Packages to Parrallelize Operations
As you progress, you’ll explore a suite of mature R packages (parallel, foreach, future). You will learn to use these packages to parallelize operations with lists, matrices, and data frames. Working through a variety of tasks, you will gain the skills to rein in the execution time of nested for loops. You will also learn how to monitor, debug, and resolve reproducibility issues of parallelized code.
Parallelize Your Existing Code
With these tools under your belt, you will be able to write parallelized code that runs significantly faster. By the time you finish, you’ll have the skills to parallelize and maintain existing code in a principled manner.
Prerequisites
Writing Efficient R CodeIntroduction to the Tidyverse1
Introduction to Parallel Programming
Learn to identify those pesky speed bottlenecks in your R code. You will run a classic numerical operation in parallel and learn to check if it helps!
2
Parallel and foreach
Use parallelism in R for a variety of situations while efficiently managing dependencies. Turn those slow loops into smooth-running machines!
3
Parallel Futures
Dive deep into the use of futures in parallel programming. Learn to process vectors, lists, and data frames in parallel, all the while keeping your code easy to maintain.
4
Troubleshooting in Parallel
Learn to manage memory for parallel processes. Make your code reproducible, and add efficient debugging to your parallel programming toolkit.
Parallel Programming in R
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FAQs
Which R packages for parallel computing does this course teach?
The course covers three established packages: parallel, foreach, and future. You will learn how each one works and when to use them for different parallelization tasks.
Is this course appropriate for someone who has never optimized R code before?
You should first complete Writing Efficient R Code, as this course assumes you can already identify performance bottlenecks and want to go further with parallel processing.
Does the course address debugging and reproducibility in parallel code?
Yes, Chapter 4 focuses on troubleshooting, including memory management for parallel processes, ensuring reproducibility, and adding efficient debugging to your parallel programming workflow.
What kinds of tasks benefit most from parallelization in R?
The course helps you identify operations that can benefit from parallelization, such as numerical operations and loop-based computations that can be distributed across multiple processors.
How is the course structured across its four chapters?
It progresses from identifying bottlenecks and basic parallel runs, to using parallel and foreach packages, then futures for cleaner code, and finally troubleshooting and debugging techniques.
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