mclapply {parallel}R Documentation

Parallel Versions of lapply and mapply using Forking


mclapply is a parallelized version of lapply, it returns a list of the same length as X, each element of which is the result of applying FUN to the corresponding element of X.

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

mcmapply is a parallelized version of mapply, and mcMap corresponds to Map.


mclapply(X, FUN, ...,
         mc.preschedule = TRUE, mc.set.seed = TRUE,
         mc.silent = FALSE, mc.cores = getOption("mc.cores", 2L),
         mc.cleanup = TRUE, mc.allow.recursive = TRUE, affinity.list = NULL)

mcmapply(FUN, ...,
         MoreArgs = NULL, SIMPLIFY = TRUE, USE.NAMES = TRUE,
         mc.preschedule = TRUE, mc.set.seed = TRUE,
         mc.silent = FALSE, mc.cores = getOption("mc.cores", 2L),
         mc.cleanup = TRUE, affinity.list = NULL)

mcMap(f, ...)



a vector (atomic or list) or an expressions vector. Other objects (including classed objects) will be coerced by as.list.


the function to be applied to (mclapply) each element of X or (mcmapply) in parallel to ....


the function to be applied in parallel to ....


For mclapply, optional arguments to FUN. For mcmapply and mcMap, vector or list inputs: see mapply.


see mapply.


if set to TRUE then the computation is first divided to (at most) as many jobs are there are cores and then the jobs are started, each job possibly covering more than one value. If set to FALSE then one job is forked for each value of X. The former is better for short computations or large number of values in X, the latter is better for jobs that have high variance of completion time and not too many values of X compared to mc.cores.


See mcparallel.


if set to TRUE then all output on ‘stdout’ will be suppressed for all parallel processes forked (‘stderr’ is not affected).


The number of cores to use, i.e. at most how many child processes will be run simultaneously. The option is initialized from environment variable MC_CORES if set. Must be at least one, and parallelization requires at least two cores.


if set to TRUE then all children that have been forked by this function will be killed (by sending SIGTERM) before this function returns. Under normal circumstances mclapply waits for the children to deliver results, so this option usually has only effect when mclapply is interrupted. If set to FALSE then child processes are collected, but not forcefully terminated. As a special case this argument can be set to the number of the signal that should be used to kill the children instead of SIGTERM.


Unless true, calling mclapply in a child process will use the child and not fork again.


a vector (atomic or list) containing the CPU affinity mask for each element of X. The CPU affinity mask describes on which CPU (core or hyperthread unit) a given item is allowed to run, see mcaffinity. To use this parameter prescheduling has to be deactivated (mc.preschedule = FALSE).


mclapply is a parallelized version of lapply, provided mc.cores > 1: for mc.cores == 1 it simply calls lapply.

By default (mc.preschedule = TRUE) the input X is split into as many parts as there are cores (currently the values are spread across the cores sequentially, i.e. first value to core 1, second to core 2, ... (core + 1)-th value to core 1 etc.) and then one process is forked to each core and the results are collected.

Without prescheduling, a separate job is forked for each value of X. To ensure that no more than mc.cores jobs are running at once, once that number has been forked the master process waits for a child to complete before the next fork.

Due to the parallel nature of the execution random numbers are not sequential (in the random number sequence) as they would be when using lapply. They are sequential for each forked process, but not all jobs as a whole. See mcparallel or the package's vignette for ways to make the results reproducible with mc.preschedule = TRUE.

Note: the number of file descriptors (and processes) is usually limited by the operating system, so you may have trouble using more than 100 cores or so (see ulimit -n or similar in your OS documentation) unless you raise the limit of permissible open file descriptors (fork will fail with error "unable to create a pipe").

Prior to R 3.4.0 and on a 32-bit platform, the serialized result from each forked process is limited to 2^31 - 1 bytes. (Returning very large results via serialization is inefficient and should be avoided.)

affinity.list can be used to run elements of X on specific CPUs. This can be helpful, if elements of X have a high variance of completion time or if the hardware architecture is heterogeneous. It also enables the development of scheduling strategies for optimizing the overall runtime of parallel jobs. If affinity.list is set, the mc.core parameter is replaced with the number of CPU ids used in the affinity masks.


For mclapply, a list of the same length as X and named by X.

For mcmapply, a list, vector or array: see mapply.

For mcMap, a list.

Each forked process runs its job inside try(..., silent = TRUE) so if errors occur they will be stored as class "try-error" objects in the return value and a warning will be given. Note that the job will typically involve more than one value of X and hence a "try-error" object will be returned for all the values involved in the failure, even if not all of them failed.


It is strongly discouraged to use these functions in GUI or embedded environments, because it leads to several processes sharing the same GUI which will likely cause chaos (and possibly crashes). Child processes should never use on-screen graphics devices.

Some precautions have been taken to make this usable in on macOS, but users of third-party front-ends should consult their documentation.

Note that tcltk counts as a GUI for these purposes since Tcl runs an event loop. That event loop is inhibited in a child process but there could still be problems with Tk graphical connections.


Simon Urbanek and R Core. The affinity.list feature by Helena Kotthaus and Andreas Lang, TU Dortmund. Derived from the multicore package formerly on CRAN.

See Also

mcparallel, pvec, parLapply, clusterMap.

simplify2array for results like sapply.


simplify2array(mclapply(rep(4, 5), rnorm))
# use the same random numbers for all values
simplify2array(mclapply(rep(4, 5), rnorm, mc.preschedule = FALSE,
                        mc.set.seed = FALSE))

## Contrast this with the examples for clusterCall
cd4.rg <- function(data, mle) MASS::mvrnorm(nrow(data), mle$m, mle$v)
cd4.mle <- list(m = colMeans(cd4), v = var(cd4))
mc <- getOption("mc.cores", 2)
run1 <- function(...) boot(cd4, corr, R = 500, sim = "parametric",
                           ran.gen = cd4.rg, mle = cd4.mle)
## To make this reproducible:
set.seed(123, "L'Ecuyer")
res <- mclapply(seq_len(mc), run1)
cd4.boot <-, res),  type = c("norm", "basic", "perc"),
        conf = 0.9, h = atanh, hinv = tanh)

## Usage of the affinity.list parameter
A <- runif(2500000,0,100)
B <- runif(2500000,0,100)
C <- runif(5000000,0,100)
first <- function(i) head(sort(i), n = 1)

# Restict all elements of X to run on CPU 1 and 2
affL <- list(c(1,2), c(1,2), c(1,2))
mclapply(list(A, A, A), first, mc.preschedule = FALSE, affinity.list = affL)

# Completion times are assumed to have a high variance
# To optimize the overall execution time elements of X are scheduled to suitable CPUs
# Assuming that the runtime for C is as long as the runtime of A plus B
# mapping: A to 1 , B to 1, C to 2
X <- list(A, B, C)
affL <- c(1, 1, 2)
mclapply(X, first, mc.preschedule = FALSE, affinity.list = affL)

[Package parallel version 3.5.0 Index]