Parallelize base-R apply 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)

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

xs <- 1:10
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)
plan(multisession) ## parallelize on local machine

ys <- lapply(xs, 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)

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()

Progress Reporting via progressr

The progressr package is specially designed to work with the Futureverse ecosystem. With progressr, progress can be reported from parallelized computations in a near-live fashion. Progress updates are propagated from the workers back to the main process, where they are relayed to provide feedback during long-running computations. This works because progress is signaled as R conditions that the future package and most future backends relay instantly.

For example:

library(futurize)
plan(multisession)
library(progressr)
handlers(global = TRUE)

xs <- 1:100
ys <- local({
  p <- progressor(along = xs)
  lapply(xs, function(x) {
    p()
    slow_fcn(x)
  })
}) |> futurize()

Note also how futurize() unwraps the expression - it descends through local() and { } to identify the lapply() call to be futurized. Using the default progress handler, the above output and progress reporting will appear as:

x = 1
x = 2
...
x = 20
  |=====                    |  20%

Known issues

The BiocGenerics package defines generic functions lapply(), sapply(), mapply(), and tapply(). These S4 generic functions override the non-generic, counterpart functions in the base package, which are only used as a fallback if there is no matching method. For example, in a vanilla R session we have that both of the following calls are identical:

y_0 <- lapply(1:3, sqrt)
y_1 <- base::lapply(1:3, sqrt)

However, if we attach the BiocGenerics package, we have that the following two calls are identical:

library(BiocGenerics)
y_2 <- lapply(1:3, sqrt)
y_3 <- BiocGenerics::lapply(1:3, sqrt)

The reason is that lapply() here is no longer base::lapply(), but the one defined by BiocGenerics, which masks the one in base. We can see this with:

find("lapply")
#> [1] "package:BiocGenerics" "package:base" 

This matters in the context of futurize. In a vanilla R session,

y <- lapply(1:3, sqrt) |> futurize()

is identical to

y <- base::lapply(1:3, sqrt) |> futurize()

However, with BiocGenerics attached, it is instead identical to:

y <- BiocGenerics::lapply(1:3, sqrt) |> futurize()

which results in:

Error in transpilers_for_package(type = type, package = ns_name, action = "make",  : 
  There are no factory functions for creating 'futurize::add-on' transpilers for package 'BiocGenerics'

The solution is to specify that it is the base version we wish to futurize, i.e.

y <- base::lapply(1:3, sqrt) |> futurize()