R: Create a multicore future whose value will be resolved...

multicore {future} | R Documentation

## Create a multicore future whose value will be resolved asynchronously in a forked 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, lazy = FALSE,
      seed = NULL, globals = TRUE, workers = availableCores(constraints =
      "multicore"), earlySignal = FALSE, label = NULL, ...)

### Arguments

`expr` | An R expression.
`envir` | The environment from where global objects should be identified.
`substitute` | If TRUE, argument `expr` is `substitute()`:ed, otherwise not.
`lazy` | If `FALSE` (default), the future is resolved eagerly (starting immediately), otherwise not.
`seed` | (optional) A L'Ecuyer-CMRG RNG seed.
`globals` | (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()`.
`workers` | 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.
`earlySignal` | Specified whether conditions should be signaled as soon as possible or not.
`label` | An optional character string label attached to the future.
`...` | Additional named elements passed to `Future()`.

### Details

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 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 sequential 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 `%<-%` will create multicore futures.

### Value

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.

### See Also

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.

### Examples

    ## Use multicore futures
    plan(multicore)

    ## A global variable
    a <- 0

    ## Create multicore 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)

[Package future version 1.9.0 Index]