+
=
The futurize package allows you to easily turn sequential code
into parallel code by piping the sequential code to the futurize()
function. Easy!
+
=
library(futurize)
plan(multisession)
library(foreach)
slow_fcn <- function(x) {
Sys.sleep(0.1) # emulate work
x^2
}
xs <- 1:1000
ys <- foreach(x = xs) %do% slow_fcn(x) |> futurize()
This vignette demonstrates how to use this approach to parallelize
functions such as foreach() and times() of the
foreach package. For example, consider:
library(foreach)
xs <- 1:1000
ys <- foreach(x = xs) %do% slow_fcn(x)
This foreach() construct is resolved sequentially. We can use
the futurize package to tell foreach to hand over the
orchestration of parallel tasks to futureverse. All we need to do is
to pass the expression to futurize() as in:
library(futurize)
library(foreach)
xs <- 1:1000
ys <- foreach(x = xs) %do% slow_fcn(x) |> 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)
Here is another example that parallelizes times() of the
foreach package via the futureverse ecosystem:
library(futurize)
library(foreach)
ys <- times(10) %do% rnorm(3) |> futurize()
The futurize() function supports parallelization of the following
foreach functions:
foreach(...) %do% { ... }times(...) %do% { ... } with seed = TRUE as the default