Parallelize base-R apply functions

Henrik Bengtsson

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The futurize package allows you to easily turn sequential code into parallel code by piping the sequential code to the futurize() function. Easy!

TL;DR

library(futurize)
plan(multisession)

slow_fcn <- function(x) {
  Sys.sleep(0.1)  # emulate work
  x^2
}

xs <- 1:1000
ys <- lapply(xs, slow_fcn) |> futurize()

Introduction

This vignette demonstrates how to use this approach to parallelize functions such as lapply(), tapply(), apply(), and replicate() in the base package, and kernapply() in the stats package. For example, consider the base R lapply() function, which is commonly used to apply a function to the elements of a vector or a list, as in:

xs <- 1:1000
ys <- lapply(xs, slow_fcn)

Here lapply() evaluates sequentially, but we can easily make it evaluate in parallel, by using:

library(futurize)
ys <- lapply(xs, slow_fcn) |> futurize()

This will distribute the calculations across the available parallel workers, given that we have set parallel workers, e.g.

plan(multisession)

The built-in multisession backend parallelizes on your local computer and it works on all operating systems. There are other parallel backends to choose from, including alternatives to parallelize locally as well as distributed across remote machines, e.g.

plan(future.mirai::mirai_multisession)

and

plan(future.batchtools::batchtools_slurm)

Kernel smoothing

library(futurize)
plan(multisession)

library(stats)

xs <- datasets::EuStockMarkets
k50 <- kernel("daniell", 50)
xs_smooth <- kernapply(xs, k = k50) |> futurize()

Supported Functions

The futurize() function supports parallelization of the common base R functions. The following base package functions are supported:

• lapply(), vapply(), sapply(), tapply()
• mapply(), .mapply(), Map()
• eapply()
• apply()
• replicate() with seed = TRUE as the default
• by()
• Filter()

The rapply() function is not supported by futurize().

The following stats package function is also supported:

• kernapply()