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Parallel Programming in R

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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4 Horas16 Videos49 Ejercicios

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Descripción del curso

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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  1. 1

    Introduction to Parallel Programming

    Gratuito

    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!

    Reproducir Capítulo Ahora
    Should we parallelize?
    50 xp
    When can you parallelize?
    50 xp
    Using parLapply()
    100 xp
    Parallelization in R
    50 xp
    Reading files in parallel
    100 xp
    Daily price ranges
    100 xp
    Measuring the benefits
    50 xp
    Bootstrapping the average maternal age
    100 xp
    Can we vectorize?
    100 xp
    Microbenchmark revenues
    100 xp

En las siguientes pistas

Desarrollador R

Colaboradores

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Jasmin Ludolf
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James Chapman
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Maarten Van den Broeck
Nabeel Imam HeadshotNabeel Imam

Data Scientist

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