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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 heures16 vidéos49 exercices

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Description du cours

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

    Gratuit

    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!

    Jouez Au Chapitre Maintenant
    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
  2. 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!

    Jouez Au Chapitre Maintenant
    parLapply in real life
    50 xp
    Running code on the cluster
    100 xp
    A parallel filter
    100 xp
    The parallel apply family
    50 xp
    Healthcare spending with clusterMap()
    100 xp
    Population growth rates with mclapply()
    100 xp
    Column operations with trading volumes
    100 xp
    Using foreach
    50 xp
    Looping with foreach
    100 xp
    foreach with variables and packages
    100 xp
    Advanced foreach operations
    50 xp
    Iterate and filter
    100 xp
    Nested foreach loops for price trends
    100 xp
  3. 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.

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    Futures
    50 xp
    Delayed square roots
    50 xp
    Futures for advertisement targets
    100 xp
    Funding social sciences with futures
    100 xp
    Mapping the future
    50 xp
    Ranking stocks with furrr
    100 xp
    Furrr type specification
    100 xp
    Advanced operations with furrr
    50 xp
    Processing multiple inputs
    100 xp
    The furrr configuration
    100 xp
    Do more with future_map()
    50 xp
    Grouped bootstrap of stock prices
    100 xp
    Row-binding future_map results
    100 xp
  4. 4

    Troubleshooting in Parallel

    Learn to manage memory for parallel processes. Make your code reproducible, and add efficient debugging to your parallel programming toolkit.

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    Monitoring and managing memory
    50 xp
    Memory profile of parLapply()
    50 xp
    Chunking the bootstrap
    100 xp
    Reproducibility in parallel
    50 xp
    Reproducible lottery results
    100 xp
    Reproduce the bootstrap
    100 xp
    Debugging in parallel
    50 xp
    The debugging steps
    100 xp
    Debugging correlations
    100 xp
    Advanced debugging
    50 xp
    The bootstrap error
    100 xp
    Catch modeling errors
    100 xp
    That's a wrap!
    50 xp
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collaborateurs

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Maarten Van den Broeck
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James Chapman
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Jasmin Ludolf

prérequis

Writing Efficient R CodeIntroduction to the Tidyverse
Nabeel Imam HeadshotNabeel Imam

Data Scientist

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