multicore: Create a multicore future whose value will be resolved asynchroneously in a parallel process

Description

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

Usage

multicore(expr, envir = parent.frame(), substitute = TRUE,
  globals = FALSE, maxCores = availableCores(), ...)

Arguments

expr

An R expression.

envir

The environment in which the evaluation is done and from which globals are obtained.

substitute

If TRUE, argument expr is substitute():ed, otherwise not.

globals

If TRUE, global objects are validated at the point in time when the future is created (always before it is resolved), that is, they identified and located. If some globals fail to be located, an informative error is generated.

maxCores

The maximum number of CPU cores that can be active at the same time before blocking.

...

Not used.

Value

A MulticoreFuture (or a EagerFuture if multicore futures are not supported).

Details

This function will block if all CPU cores are occupied and will be unblocked as soon as one of the already running multicore futures is resolved. For the total number of CPU cores availble to the current R process, see availableCores().

Not all systems support multicore futures. For instance, it is not supported on Microsoft Windows. Trying to create multicore futures on non-supported systems will silently fall back to using eager futures, which effectively corresponds to a multicore future that can handle one parallel process (the current one) before blocking.

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 %<=%< a=""> will create multicore futures.

Examples

Run this code

## A global variable
a <- 0

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

## A multicore future is evaluated in a separated
## 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)

Run the code above in your browser using DataLab

Get 50% off unlimited learning