future (version 1.21.0)

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


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.


  workers = availableCores(),
  lazy = FALSE,
  rscript_libs = .libPaths(),
  envir = parent.frame()



Additional arguments passed to 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.


If FALSE (default), the future is resolved eagerly (starting immediately), otherwise not.


A character vector of R package library folders that the workers should use. The default is .libPaths() so that multisession workers inherits the same library path as the main R session. To avoid this, use plan(multisession, ..., rscript_libs = NULL). Important: Note that the library path is set on the workers when they are created, i.e. when plan(multisession) is called. Any changes to .libPaths() in the main R session after the workers have been created will have no effect. This is passed down as-is to parallelly::makeClusterPSOCK().


The environment from where global objects should be identified.


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.


The background R sessions (the "workers") are created using makeClusterPSOCK().

The multisession() 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 parallelly::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 %<-% will create multisession futures.

See Also

For processing in multiple forked R sessions, see multicore futures.

Use parallelly::availableCores() to see the total number of cores that are available for the current R session.


## Use multisession futures

## A global variable
a <- 0

## Create 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

v <- value(f)
stopifnot(v == 0)

## Explicitly close multisession workers by switching plan
# }