课程
Parallel Programming in R
中级技能水平
更新时间 2024年6月
RProgramming4 小时16 视频49 练习3,950 经验值成就声明
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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.
先决条件
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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