| clusterApply {parallel} | R Documentation |
These functions provide several ways to parallelize computations using a cluster.
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, ..., chunk.size = NULL)
parSapply(cl = NULL, X, FUN, ..., simplify = TRUE,
USE.NAMES = TRUE, chunk.size = NULL)
parApply(cl = NULL, X, MARGIN, FUN, ..., chunk.size = NULL)
parRapply(cl = NULL, x, FUN, ..., chunk.size = NULL)
parCapply(cl = NULL, x, FUN, ..., chunk.size = NULL)
parLapplyLB(cl = NULL, X, fun, ..., chunk.size = NULL)
parSapplyLB(cl = NULL, X, FUN, ..., simplify = TRUE,
USE.NAMES = TRUE, chunk.size = NULL)
cl |
a cluster object, created by this package or by package
snow. If |
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 |
... |
additional arguments to pass to |
MoreArgs |
additional arguments for |
RECYCLE |
logical; if true shorter arguments are recycled. |
X |
A vector (atomic or list) for |
chunk.size |
scalar number; number of invocations of |
MARGIN |
vector specifying the dimensions to use. |
simplify, USE.NAMES |
logical; see |
SIMPLIFY |
logical; see |
.scheduling |
should tasks be statically allocated to nodes or dynamic load-balancing used? |
clusterCall calls a function fun with identical
arguments ... on each node.
clusterEvalQ evaluates a literal expression on each cluster
node. It is a parallel version of evalq, and is a
convenience function invoking clusterCall.
clusterApply calls fun on the first node with
arguments x[[1]] and ..., on the second node with
x[[2]] and ..., and so on, recycling nodes as needed.
clusterApplyLB is a load balancing version of
clusterApply. If the length n of x is not
greater than the number of nodes p, then a job is sent to
n nodes. Otherwise the first p jobs are placed in order
on the p 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. This means that
simulations that assign RNG streams to nodes will not be reproducible.
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. Chunks of
computation are statically allocated to nodes using clusterApply.
By default, the number of chunks is the same as the number of nodes.
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. Chunks of
computation are allocated dynamically to nodes using
clusterApplyLB. From R 3.5.0, the default number of chunks is
twice the number of nodes. Before R 3.5.0, the (fixed) number of chunks
was the same as the number of nodes. As for clusterApplyLB,
with load balancing the node that executes a particular job is
non-deterministic and simulations that assign RNG streams to nodes
will not be reproducible.
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.
A chunk size of 0 with static scheduling uses the default (one
chunk per node). With dynamic scheduling, chunk size of 0 has the
same effect as 1 (one invocation of FUN/fun per
chunk).
For clusterCall, clusterEvalQ and clusterSplit, a
list with one element per node.
For clusterApply and clusterApplyLB, a list the same
length as x.
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.
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.
Luke Tierney and R Core.
Derived from the snow package.
## Use option cl.cores 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)