multicore | R Documentation |
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()
)
... |
Additional arguments passed to |
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. |
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)
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.
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
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.
## 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)
v <- value(f)
print(v)
stopifnot(v == 0)
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