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"),
  envir = parent.frame()
)

Arguments

...

Additional arguments passed to Future().

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.

envir

The environment from where global objects should be identified.

Value

A MulticoreFuture. 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

See also

For processing in multiple background R sessions, see multisession futures.

Use parallelly::availableCores() to see the total number of cores that are available for the current R session. Use availableCores("multicore") > 1L to check whether multicore futures are supported or not on the current system.

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)