R: Parallelize a Vector Map Function using Forking

pvec {parallel} | R Documentation

## Parallelize a Vector Map Function using Forking

### Description

`pvec` parellelizes the execution of a function on vector elements by splitting the vector and submitting each part to one core. The function must be a vectorized map, i.e. it takes a vector input and creates a vector output of exactly the same length as the input which doesn't depend on the partition of the vector.

It relies on forking and hence is not available on Windows unless `mc.cores = 1`.

### Usage

    pvec(v, FUN, ..., mc.set.seed = TRUE, mc.silent = FALSE,
         mc.cores = getOption("mc.cores", 2L), mc.cleanup = TRUE)

### Arguments

`v` | vector to operate on
`FUN` | function to call on each part of the vector
`...` | any further arguments passed to `FUN` after the vector
`mc.set.seed` | See `mcparallel`.
`mc.silent` | if set to `TRUE` then all output on ‘stdout’ will be suppressed for all parallel processes forked (‘stderr’ is not affected).
`mc.cores` | The number of cores to use, i.e. at most how many child processes will be run simultaneously. Must be at least one, and at least two for parallel operation. The option is initialized from environment variable MC_CORES if set.
`mc.cleanup` | See the description of this argument in `mclapply`.

### Details

`pvec` parallelizes `FUN(x, ...)` where `FUN` is a function that returns a vector of the same length as `x`. `FUN` must also be pure (i.e., without side-effects) since side-effects are not collected from the parallel processes. The vector is split into nearly identically sized subvectors on which `FUN` is run. Although it is in principle possible to use functions that are not necessarily maps, the interpretation would be case-specific as the splitting is in theory arbitrary (a warning is given in such cases).

The major difference between `pvec` and `mclapply` is that `mclapply` will run `FUN` on each element separately whereas `pvec` assumes that `c(FUN(x[1]), FUN(x[2]))` is equivalent to `FUN(x[1:2])` and thus will split into as many calls to `FUN` as there are cores (or elements, if fewer), each handling a subset vector. This makes it more efficient than `mclapply` but requires the above assumption on `FUN`.

If `mc.cores == 1` this evaluates `FUN(v, ...)` in the current process.

### Value

The result of the computation – in a successful case it should be of the same length as `v`. If an error occurred or the function was not a map the result may be shorter or longer, and a warning is given.

### Note

Due to the nature of the parallelization, error handling does not follow the usual rules since errors will be returned as strings and results from killed child processes will show up simply as non-existent data. Therefore it is the responsibility of the user to check the length of the result to make sure it is of the correct size. `pvec` raises a warning if that is the case since it does not know whether such an outcome is intentional or not.

See `mcfork` for the inadvisability of using this with GUI front-ends.

### Author(s)

Simon Urbanek and R Core.

Derived from the multicore package.

### See Also

`mcparallel`, `mclapply`, `parLapply`, `clusterMap`.

### Examples

    x <- pvec(1:1000, sqrt)
    stopifnot(all(x == sqrt(1:1000)))

    # One use is to convert date strings to unix time in large datasets
    # as that is a relatively slow operation.
    # So let's get some random dates first
    # (A small test only with 2 cores: set options("mc.cores")
    # and increase N for a larger-scale test.)
    N <- 1e4
    dates <- sprintf('%04d-%02d-%02d', as.integer(2000+rnorm(N)),
                     as.integer(runif(N, 1, 12)), as.integer(runif(N, 1, 28)))

    system.time(a <- as.POSIXct(dates))
    # This ought to be faster, but system overhead can be high
    # - and is on OS X Lion
    system.time(b <- pvec(dates, as.POSIXct))
    stopifnot(all(a == b))

    # using mclapply for this would much slower because each value
    # will require a separate call to as.POSIXct()
    # as lapply(dates, as.POSIXct) does
    system.time(c <- unlist(mclapply(dates, as.POSIXct))

[Package parallel version 2.15.1 Index]