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(
expr,
envir = parent.frame(),
substitute = TRUE,
lazy = FALSE,
seed = NULL,
globals = TRUE,
workers = availableCores(constraints = "multicore"),
earlySignal = FALSE,
label = NULL,
...
)An R expression.
The environment from where global objects should be identified.
If TRUE, argument expr is
substitute():ed, otherwise not.
If FALSE (default), the future is resolved eagerly (starting immediately), otherwise not.
(optional) If TRUE, the random seed, that is, the state of the
random number generator (RNG) will be set such that statistically sound
random numbers are produced (also during parallelization).
If FALSE, it is assumed that the future expression does neither need nor
use random numbers generation.
To use a fixed random seed, specify a L'Ecuyer-CMRG seed (seven integer)
or a regular RNG seed (a single integer).
Furthermore, if FALSE, then the future will be monitored to make sure it
does not use random numbers. If it does and depending on the value of
option future.rng.misUse, the check is
ignored, an informative warning, or error will be produced.
If seed is NULL (default), then the effect is as with seed = FALSE
but without the RNG check being performed.
(optional) a logical, a character vector, or a named list
to control how globals are handled.
For details, see section 'Globals used by future expressions'
in the help for future().
A positive numeric scalar or a function specifying the maximum number of parallel futures that can be active at the same time before blocking. If a function, it is called without arguments when the future is created and its value is used to configure the workers. The function should return a numeric scalar.
Specified whether conditions should be signaled as soon as possible or not.
An optional character string label attached to the future.
Additional named elements passed to Future().
A MulticoreFuture
If workers == 1, then all processing using done in the
current/main R session and we therefore fall back to using
an sequential future. This is also the case whenever multicore
processing is not supported, e.g. on Windows.
This function will block if all cores are occupied and
will be unblocked as soon as one of the already running
multicore futures is resolved. For the total number of
cores available including the current/main R process, see
availableCores().
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 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.
The preferred way to create an multicore future is not to call
this function directly, but to register it via
plan(multicore) such that it becomes the default
mechanism for all futures. After this future()
and %<-% will create multicore futures.
For processing in multiple background R sessions, see multisession futures. For multicore processing with fallback to multisession where the former is not supported, see multiprocess futures.
Use 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.
# NOT RUN {
## 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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