clusterApply {parallel} | R Documentation

## Apply Operations using Clusters

### Description

These functions provide several ways to parallelize computations using a cluster.

### Usage

    clusterCall(cl = NULL, fun, ...)
    clusterApply(cl = NULL, x, fun, ...)
    clusterApplyLB(cl = NULL, x, fun, ...)
    clusterEvalQ(cl = NULL, expr)
    clusterExport(cl = NULL, varlist, envir = .GlobalEnv)
    clusterMap(cl = NULL, fun, ..., MoreArgs = NULL, RECYCLE = TRUE,
               SIMPLIFY = FALSE, USE.NAMES = TRUE,
               .scheduling = c("static", "dynamic"))
    clusterSplit(cl = NULL, seq)

    parLapply(cl = NULL, X, fun, ...)
    parSapply(cl = NULL, X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE)
    parApply(cl = NULL, X, MARGIN, FUN, ...)
    parRapply(cl = NULL, x, FUN, ...)
    parCapply(cl = NULL, x, FUN, ...)

    parLapplyLB(cl = NULL, X, fun, ...)
    parSapplyLB(cl = NULL, X, FUN, ..., simplify = TRUE, USE.NAMES = TRUE)

### Arguments

`cl` | a cluster object, created by this package or by package snow. If `NULL`, use the registered default cluster.
`fun, FUN` | function or character string naming a function.
`expr` | expression to evaluate.
`seq` | vector to split.
`varlist` | character vector of names of objects to export.
`envir` | environment from which t export variables
`x` | a vector for `clusterApply` and `clusterApplyLB`, a matrix for `parRapply` and `parCapply`.
`...` | additional arguments to pass to `fun` or `FUN`: beware of partial matching to earlier arguments.
`MoreArgs` | additional arguments for `fun`.
`RECYCLE` | logical; if true shorter arguments are recycled.
`X` | A vector (atomic or list) for `parLapply` and `parSapply`, an array for `parApply`.
`MARGIN` | vector specifying the dimensions to use.
`simplify, USE.NAMES` | logical; see `sapply`.
`SIMPLIFY` | logical; see `mapply`.
`.scheduling` | should tasks be statically allocated to nodes or dynamic load-balancing used?

### Details

`clusterCall` calls a function `fun` with identical arguments `...` on each node.

`clusterEvalQ` evaluates a literal expression on each cluster node. It a parallel version of `evalq`, and is a convenience function invoking `clusterCall`.

`clusterApply` calls `fun` on the first node with arguments `seq[[1]]` and `...`, on the second node with `seq[[2]]` and `...`, and so on, recycling nodes as needed.

`clusterApplyLB` is a load balancing version of `clusterApply`. If the length `p` of `seq` is not greater than the number of nodes `n`, then a job is sent to `p` nodes. Otherwise the first `n` jobs are placed in order on the `n` nodes. When the first job completes, the next job is placed on the node that has become free; this continues until all jobs are complete. Using `clusterApplyLB` can result in better cluster utilization than using `clusterApply`, but increased communication can reduce performance. Furthermore, the node that executes a particular job is non-deterministic.

`clusterMap` is a multi-argument version of `clusterApply`, analogous to `mapply` and `Map`. If `RECYCLE` is true shorter arguments are recycled (and either none or all must be of length zero); otherwise, the result length is the length of the shortest argument. Nodes are recycled if the length of the result is greater than the number of nodes. (`mapply` always uses `RECYCLE = TRUE`, and has argument `SIMPLIFY = TRUE`. `Map` always uses `RECYCLE = TRUE`.)

`clusterExport` assigns the values on the master R process of the variables named in `varlist` to variables of the same names in the global environment (aka ‘workspace’) of each node. The environment on the master from which variables are exported defaults to the global environment.

`clusterSplit` splits `seq` into a consecutive piece for each cluster and returns the result as a list with length equal to the number of nodes. Currently the pieces are chosen to be close to equal in length: the computation is done on the master.

`parLapply`, `parSapply`, and `parApply` are parallel versions of `lapply`, `sapply` and `apply`. `parLapplyLB`, `parSapplyLB` are load-balancing versions, intended for use when applying `FUN` to different elements of `X` takes quite variable amounts of time, and either the function is deterministic or reproducible results are not required.

`parRapply` and `parCapply` are parallel row and column `apply` functions for a matrix `x`; they may be slightly more efficient than `parApply` but do less post-processing of the result.

### Value

For `clusterCall`, `clusterEvalQ` and `clusterSplit`, a list with one element per node.

For `clusterApply` and `clusterApplyLB`, a list the same length as `seq`.

`clusterMap` follows `mapply`.

`clusterExport` returns nothing.

`parLapply` returns a list the length of `X`.

`parSapply` and `parApply` follow `sapply` and `apply` respectively.

`parRapply` and `parCapply` always return a vector. If `FUN` always returns a scalar result this will be of length the number of rows or columns: otherwise it will be the concatenation of the returned values.

An error is signalled on the master if any of the workers produces an error.

### Note

These functions are almost identical to those in package snow.

Two exceptions: `parLapply` has argument `X` not `x` for consistency with `lapply`, and `parSapply` has been updated to match `sapply`.

### Author(s)

Luke Tierney and R Core.

Derived from the snow package.

### Examples

    ## Use option cl.core to choose an appropriate cluster size.
    cl <- makeCluster(getOption("cl.cores", 2))

    clusterApply(cl, 1:2, get("+"), 3)
    xx <- 1
    clusterExport(cl, "xx")
    clusterCall(cl, function(y) xx + y, 2)

    ## Use clusterMap like an mapply example
    clusterMap(cl, function(x,y) seq_len(x) + y,
              c(a =  1, b = 2, c = 3), c(A = 10, B = 0, C = -10))

    parSapply(cl, 1:20, get("+"), 3)

    ## A bootstrapping example, which can be done in many ways:
    clusterEvalQ(cl, {
      ## set up each worker.  Could also use clusterExport()
      library(boot)
      cd4.rg <- function(data, mle) MASS::mvrnorm(nrow(data), mle$m, mle$v)
      cd4.mle <- list(m = colMeans(cd4), v = var(cd4))
      NULL
    })
    res <- clusterEvalQ(cl, boot(cd4, corr, R = 100,
                        sim = "parametric", ran.gen = cd4.rg, mle = cd4.mle))
    library(boot)
    cd4.boot <- do.call(c, res)
    boot.ci(cd4.boot,  type = c("norm", "basic", "perc"),
            conf = 0.9, h = atanh, hinv = tanh)
    stopCluster(cl)

    ## or
    library(boot)
    run1 <- function(...) {
       library(boot)
       cd4.rg <- function(data, mle) MASS::mvrnorm(nrow(data), mle$m, mle$v)
       cd4.mle <- list(m = colMeans(cd4), v = var(cd4))
       boot(cd4, corr, R = 500, sim = "parametric",
            ran.gen = cd4.rg, mle = cd4.mle)
    }
    cl <- makeCluster(mc <- getOption("cl.cores", 2))
    ## to make this reproducible
    clusterSetRNGStream(cl, 123)
    cd4.boot <- do.call(c, parLapply(cl, seq_len(mc), run1))
    boot.ci(cd4.boot,  type = c("norm", "basic", "perc"),
            conf = 0.9, h = atanh, hinv = tanh)
    stopCluster(cl)

[Package parallel version 2.15.1 Index]