Parallel Programming in R

Intermediate

Updated 12/2024

Unlock the power of parallel computing in R. Enhance your data analysis skills, speed up computations, and process large datasets effortlessly.

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Course Description

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 Code Introduction to the Tidyverse

Introduction to Parallel Programming

Start Chapter

Should we parallelize?

Course Description

Speed Up Your Code with Parallel Programming

Learn the Key Concepts

Use R Packages to Parrallelize Operations

Parallelize Your Existing Code

Earn Statement of Accomplishment

Join over .css-nklxlk{color:var(--wf-brand--main, #03EF62);}15 million learners and start Parallel Programming in R today!

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Join over 15 million learners and start Parallel Programming in R today!