FeaturedUdemy
Parallel Programming in R
4.8(75)1k studentsINTERMEDIATEEnglish
Korshub's take
Editor's Take
While R is a cornerstone of modern data analysis, computationally intensive scripts can become execution bottlenecks. This course shows how to unlock your machine's full potential by distributing tasks across multiple processor cores.
Who It's For
Designed for intermediate R programmers seeking to accelerate slow computations, optimize loops, and write reproducible parallel code.
Key Takeaways
- Benchmark and profile standard operations such as bootstraps and function mappings to spot parallelization opportunities.
- Harness core R packages including parallel, foreach, and future.
- Distribute workload across lists, matrices, and data frames while resolving nested loop delays and debugging reproducibility challenges.
About this course
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.
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