Create a multicore future whose value will be resolved asynchronously in a forked parallel process

A multicore future is a future that uses multicore evaluation, which means that its value is computed and resolved in parallel in another process.

multicore(
  ...,
  workers = availableCores(constraints = "multicore"),
  gc = FALSE,
  earlySignal = FALSE,
  maxSizeOfObjects = NULL
)

Arguments

workers: The number of parallel processes to use. If a function, it is called without arguments when the future is created and its value is used to configure the workers.
gc: If TRUE, the garbage collector run (in the process that evaluated the future) only after the value of the future is collected. Exactly when the values are collected may depend on various factors such as number of free workers and whether earlySignal is TRUE (more frequently) or FALSE (less frequently). Some types of futures ignore this argument.
earlySignal: Specified whether conditions should be signaled as soon as possible or not.
maxSizeOfObjects: The maximum allowed total size, in bytes, of all objects to and from the parallel worker allows. This can help to protect against unexpectedly large data transfers between the parent process and the parallel workers - data that is often transferred over the network, which sometimes also includes the internet. For instance, if you sit at home and have set up a future backend with workers running remotely at your university or company, then you might want to use this protection to avoid transferring giga- or terabytes of data without noticing. (Default: \(500 \cdot 1024^2\) bytes = 500 MiB, unless overridden by a FutureBackend subclass, or by R option future.globals.maxSize (sic!))
...: Additional named elements to Future().

Value

A Future. If workers == 1, then all processing using done in the current/main R session and we therefore fall back to using a sequential future. To override this fallback, use workers = I(1). This is also the case whenever multicore processing is not supported, e.g. on Windows.

Details

This function is not meant to be called directly. Instead, the typical usages are:

# Evaluate futures in parallel on the local machine via as many forked
# processes as available to the current R process
plan(multicore)

# Evaluate futures in parallel on the local machine via two forked processes
plan(multicore, workers = 2)

Support for forked ("multicore") processing

Not all operating systems support process forking and thereby not multicore futures. For instance, forking is not supported on Microsoft Windows. Moreover, process forking may break some R environments such as RStudio. Because of this, the future package disables process forking also in such cases. See parallelly::supportsMulticore() for details. Trying to create multicore futures on non-supported systems or when forking is disabled will result in multicore futures falling back to becoming sequential futures. If used in RStudio, there will be an informative warning:

> plan(multicore)
Warning message:
In supportsMulticoreAndRStudio(...) :
  [ONE-TIME WARNING] Forked processing ('multicore') is not supported when
running R from RStudio because it is considered unstable. For more details,
how to control forked processing or not, and how to silence this warning in
future R sessions, see ?parallelly::supportsMulticore

Examples

## Use multicore futures
plan(multicore)

## A global variable
a <- 0

## Create future (explicitly)
f <- future({
  b <- 3
  c <- 2
  a * b * c
})

## A multicore future is evaluated in a separate forked
## process.  Changing the value of a global variable
## will not affect the result of the future.
a <- 7
print(a)
#> [1] 7

v <- value(f)
print(v)
#> [1] 0
stopifnot(v == 0)