R: Evaluate an R Expression Asynchronously in a Separate Process

mcparallel {parallel} | R Documentation

## Evaluate an R Expression Asynchronously in a Separate Process

### Description

These functions are based on forking and so are not available on Windows.

`mcparallel` starts a parallel R process which evaluates the given expression.

`mccollect` collects results from one or more parallel processes.

### Usage

    mcparallel(expr, name, mc.set.seed = TRUE, silent = FALSE)

    mccollect(jobs, wait = TRUE, timeout = 0, intermediate = FALSE)

### Arguments

`expr` | expression to evaluate (do not use any on-screen devices or GUI elements in this code).
`name` | an optional name (character vector of length one) that can be associated with the job.
`mc.set.seed` | logical: see section ‘Random numbers’.
`silent` | if set to `TRUE` then all output on stdout will be suppressed (stderr is not affected).
`jobs` | list of jobs (or a single job) to collect results for. Alternatively `jobs` can also be an integer vector of process IDs. If omitted `collect` will wait for all currently existing children.
`wait` | if set to `FALSE` it checks for any results that are available within `timeout` seconds from now, otherwise it waits for all specified jobs to finish.
`timeout` | timeout (in seconds) to check for job results – applies only if `wait` is `FALSE`.
`intermediate` | `FALSE` or a function which will be called while `collect` waits for results. The function will be called with one parameter which is the list of results received so far.

### Details

`mcparallel` evaluates the `expr` expression in parallel to the current R process. Everything is shared read-only (or in fact copy-on-write) between the parallel process and the current process, i.e. no side-effects of the expression affect the main process. The result of the parallel execution can be collected using `mccollect` function.

`mccollect` function collects any available results from parallel jobs (or in fact any child process). If `wait` is `TRUE` then `collect` waits for all specified jobs to finish before returning a list containing the last reported result for each job. If `wait` is `FALSE` then `mccollect` merely checks for any results available at the moment and will not wait for jobs to finish. If `jobs` is specified, jobs not listed there will not be affected or acted upon.

Note: If `expr` uses low-level multicore functions such as `sendMaster` a single job can deliver results multiple times and it is the responsibility of the user to interpret them correctly. `mccollect` will return `NULL` for a terminating job that has sent its results already after which the job is no longer available.

### Value

`mcparallel` returns an object of the class `"parallelJob"` which inherits from `"childProcess"` (see the ‘Value’ section of the help for `mcfork`). If argument `name` was supplied this will have an additional component `name`.

`mccollect` returns any results that are available in a list. The results will have the same order as the specified jobs. If there are multiple jobs and a job has a name it will be used to name the result, otherwise its process ID will be used. If none of the specified children are still running, it returns `NULL`.

### Random numbers

If `mc.set.seed = FALSE`, the child process has the same initial random number generator (RNG) state as the current R session. If the RNG has been used (or `.Random.seed` was restored from a saved workspace), the child will start drawing random numbers at the same point as the current session. If the RNG has not yet been used, the child will set a seed based on the time and process ID when it first uses the RNG: this is pretty much guaranteed to give a different random-number stream from the current session and any other child process.

The behaviour with `mc.set.seed = TRUE` is different only if `RNGkind("L'Ecuyer-CMRG")` has been selected. Then each time a child is forked it is given the next stream (see `nextRNGStream`). So if you select that generator, set a seed and call `mc.reset.stream` just before the first use of `mcparallel` the results of simulations will be reproducible provided the same tasks are given to the first, second, ... forked process.

### Note

Package multicore also exported functions `collect` and `parallel`. These names are easily masked (for example package lattice also has a function `parallel`) and they are not supplied in this package.

### Author(s)

Simon Urbanek and R Core.

Derived from the multicore package (but with different handling of the RNG stream).

### See Also

`pvec`, `mclapply`

### Examples

    p <- mcparallel(1:10)
    q <- mcparallel(1:20)
    # wait for both jobs to finish and collect all results
    res <- mccollect(list(p, q))

    p <- mcparallel(1:10)
    mccollect(p, wait=FALSE, 10) # will retrieve the result (since it's fast)
    mccollect(p, wait=FALSE)     # will signal the job as terminating
    mccollect(p, wait=FALSE)     # there is no longer such a job

    # a naive parallel lapply can be created using mcparallel alone:
    jobs <- lapply(1:10, function(x) mcparallel(rnorm(x), name = x))
    mccollect(jobs)

[Package parallel version 2.15.1 Index]