future (version 1.0.1)

multisession: Create a multisession future whose value will be resolved asynchroneously in a parallel R session

Description

A multisession future is a future that uses multisession evaluation, which means that its value is computed and resolved in parallel in another R session.

Usage

multisession(expr, envir = parent.frame(), substitute = TRUE, persistent = FALSE, workers = availableCores(), gc = FALSE, earlySignal = FALSE, ...)

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.

persistent

If FALSE, the evaluation environment is cleared from objects prior to the evaluation of the future.

workers

The maximum number of multisession futures that can be active at the same time before blocking.

gc

If TRUE, the garbage collector run after the future is resolved (in the process that evaluated the future).

earlySignal

Specified whether conditions should be signaled as soon as possible or not.

...

Not used.

Value

A MultisessionFuture. If workers == 1, then all processing using done in the current/main R session and we therefore fall back to using a lazy future.

Known issues

In the current implementation, all background R sessions are allocated and launched in the background as soon as the first multisession future is created. This means that more R sessions may be running than what will ever be used. The reason for this is that background sessions are currently created using makeCluster(), which requires that all R sessions are created at once.

Details

This function will block if all available R session are occupied and will be unblocked as soon as one of the already running multisession futures is resolved. For the total number of R sessions available including the current/main R process, see availableCores().

A multisession future is a special type of cluster future.

The preferred way to create an multisession future is not to call this function directly, but to register it via plan(multisession) such that it becomes the default mechanism for all futures. After this future() and %<=%< a=""> will create multisession futures.

See Also

For processing in multiple forked R sessions, see multicore 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.

Examples

## Multisession futures gives an error on R CMD check on
## Windows (but not Linux or OS X) for unknown reasons.
## The same code works in package tests.


## Use multisession futures
plan(multisession)

## A global variable
a <- 0

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

## A multisession future is evaluated in a separate R session.
## 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)