Parallel Computing in R

R runs on one core by default, but many tasks are made of independent pieces. Parallel computing spreads those pieces across cores to finish the same work in a fraction of the time.

Learn Parallel Computing in R in our free R course — a beginner-friendly interactive lesson with worked examples, a practice exercise and a quick reference.

Part of the free R course at LearnCodingFast — hands-on lessons with examples you run in your browser, plus practice exercises and a quick quiz.

By the end of this lesson you'll use the parallel package, run loops with foreach and doParallel, reach for future and furrr, and know when parallelism actually pays off.

What You'll Learn in This Lesson

1️⃣ How Many Cores? detectCores()

The base parallel package ships with R. detectCores() reports how many logical cores you have; leaving one free keeps the rest of the system responsive.

2️⃣ Clusters and parLapply()

makeCluster() starts worker processes; parLapply() runs a function over a list across them — a parallel lapply() . Always finish with stopCluster() .

3️⃣ Loops with foreach + doParallel

foreach writes loops that return results; doParallel registers a backend so %dopar% runs iterations in parallel. .combine sets how results are glued together.

4️⃣ One API: future & furrr

The future framework lets you choose a backend once with plan() ; furrr 's future_map() is a parallel drop-in for purrr::map() .

Your turn. Fill in the # TODO blank, run it, and compare with the expected output.

Use foreach with a parallel backend to run four independent simulations and collect their means. Independent random draws are the textbook embarrassingly parallel task.

📋 Quick Reference — Parallel R

Practice quiz

Which base R function reports the number of CPU cores?

  • detectCores()
  • numCores()
  • cpus()
  • coreCount()

Answer: detectCores(). parallel::detectCores() returns the number of logical CPU cores available.

What does makeCluster() do in the parallel package?

  • Deletes a cluster
  • Reads a CSV
  • Creates a set of worker processes to run tasks in parallel
  • Plots a dendrogram

Answer: Creates a set of worker processes to run tasks in parallel. makeCluster() starts worker R processes that the main session can send work to.

What is parLapply()?

  • A string function
  • A parallel version of lapply() that runs over a cluster
  • A function to plot lists
  • A way to load packages

Answer: A parallel version of lapply() that runs over a cluster. parLapply() applies a function over a list across cluster workers, like a parallel lapply().

Why should you call stopCluster() when finished?

  • It speeds up detectCores()
  • To save the plot
  • To sort the results
  • To free the worker processes and their resources

Answer: To free the worker processes and their resources. stopCluster() shuts down the workers so their memory and processes are released.

Which combination provides the foreach loop with parallel backends?

  • foreach and doParallel
  • dplyr and tidyr
  • ggplot2 and plotly
  • knitr and rmarkdown

Answer: foreach and doParallel. foreach defines the loop and doParallel registers a parallel backend for it.

In a foreach loop, what does the .combine argument control?

  • The number of cores
  • How the per-iteration results are combined (e.g. c, rbind)
  • The random seed
  • The package to load

Answer: How the per-iteration results are combined (e.g. c, rbind). .combine tells foreach how to aggregate results, such as c, rbind, or cbind.

Which modern packages offer a unified parallel API across backends?

  • stringr and lubridate
  • readr and haven
  • MASS and car
  • future and furrr

Answer: future and furrr. The future framework and furrr (future_map) give a consistent API over many backends.

What kind of task benefits most from parallelism?

  • Tasks where each step depends on the previous one
  • Tiny tasks that finish instantly
  • Embarrassingly parallel tasks with independent pieces
  • Reading a single small file

Answer: Embarrassingly parallel tasks with independent pieces. Independent, embarrassingly parallel tasks split cleanly across cores with little coordination.

Why might parallelizing a very fast task actually be slower?

  • R cannot run in parallel
  • The overhead of starting workers and copying data exceeds the savings
  • detectCores returns zero
  • Parallel code always errors

Answer: The overhead of starting workers and copying data exceeds the savings. Spawning workers and shipping data has overhead; for quick tasks it can outweigh the speedup.

What does furrr's future_map() parallelize?

  • A purrr-style map() over a parallel backend
  • A SQL query
  • A regex match
  • A ggplot

Answer: A purrr-style map() over a parallel backend. future_map() is the parallel drop-in for purrr::map(), running iterations across workers.