Parallelize 'purrr' functions

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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)
library(purrr)

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

xs <- 1:10
ys <- xs |> map(slow_fcn) |> futurize()

Introduction

This vignette demonstrates how to use this approach to parallelize purrr functions such as map(), map_dbl(), and walk().

The purrr map() function is commonly used to apply a function to the elements of a vector or a list. For example,

library(purrr)
xs <- 1:1000
ys <- map(xs, slow_fcn)

or equivalently using pipe syntax

xs <- 1:1000
ys <- xs |> map(slow_fcn)

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

library(purrr)

library(futurize)
plan(multisession) ## parallelize on local machine

xs <- 1:1000
ys <- xs |> map(slow_fcn) |> futurize()
#> x = 1
#> x = 2
#> x = 3
#> ...
#> x = 10

Note how messages produced on parallel workers are relayed as-is back to the main R session as they complete. Not only messages, but also warnings and other types of conditions are relayed back as-is. Likewise, standard output produced by cat(), print(), str(), and so on is relayed in the same way. This is a unique feature of Futureverse - other parallel frameworks in R, such as parallel, foreach with doParallel, and BiocParallel, silently drop standard output, messages, and warnings produced on workers. With futurize, your code behaves the same whether it runs sequentially or in parallel: nothing is lost in translation.

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)

Another example is:

library(purrr)
library(futurize)
plan(future.mirai::mirai_multisession)

ys <- 1:10 |>
        map(rnorm, n = 10) |> futurize(seed = TRUE) |>
        map_dbl(mean) |> futurize()

Supported Functions

The futurize() function supports parallelization of the following purrr functions:

• map(), map_chr(), map_dbl(), map_int(), map_lgl(), map_raw(), map_dfr(), map_dfc(), walk()
• map2(), map2_chr(), map2_dbl(), map2_int(), map2_lgl(), map2_raw(), map2_dfr(), map2_dfc(), walk2()
• pmap(), pmap_chr(), pmap_dbl(), pmap_int(), pmap_lgl(), pmap_raw(), pmap_dfr(), pmap_dfc(), pwalk()
• imap(), imap_chr(), imap_dbl(), imap_int(), imap_lgl(), imap_raw(), imap_dfr(), imap_dfc(), iwalk()
• modify(), modify_if(), modify_at()
• map_if(), map_at()
• invoke_map(), invoke_map_chr(), invoke_map_dbl(), invoke_map_int(), invoke_map_lgl(), invoke_map_raw(), invoke_map_dfr(), invoke_map_dfc()

Progress Reporting via progressr

For progress reporting, please see the [progressr] package. It is specially designed to work with the Futureverse ecosystem and provide progress updates from parallelized computations in a near-live fashion. See the vignette("futurize-11-apply", package = "futurize") for more details and an example.