An interface primarily designed to apply some function fn in parallel on columns of a matrix, although other uses are possible. Depending on the nb_cores argument, parallel or serial computation is performed. A socket cluster is used by default for parallel computations, but a fork cluster can be requested on linux and alike operating systems by using argument cluster_args=list(type="FORK").
dopar(newresp, fn, nb_cores = NULL, fit_env, control = list(),
cluster_args = NULL, debug. = FALSE, iseed = NULL,
showpbar = eval(spaMM.getOption("barstyle")),
pretest_cores =NULL, ...)The result of calling foreach, pbapply or mclapply, as dependent on the control argument. A side-effect of dopar is to show a progress bar that informs about the type of parallelisation performed: a default "=" character for fork clusters, and otherwise"P" for parallel computation via foreach and doSNOW, "p" for parallel computation via pbapply, and "s" for serial computation via pbapply.
A matrix on whose columns fn will be applied (e.g., as used internally in spaMM, the return value of a simulate.HLfit() call); or an integer, then converted to a trivial matrix matrix(seq(newresp),ncol=newresp,nrow=1).
Function whose first argument is named y. The function will be applied for y taken to be each column of newresp.
Integer. Number of cores to use for parallel computations. If >1, a cluster of nb_cores nodes is used. Otherwise, no parallel computation is performed.
(for socket clusters only:) An environment, or a list, containing variables to be exported on the nodes of the cluster (by parallel::clusterExport); e.g., list(bar=bar) to pass object bar to each node. The argument control(.errorhandling = "pass"), below, is useful to find out missing variables.
A list following the foreach control syntax, even if foreach is not used. In particular,
for socket clusters, with doSNOW attached, foreach is called with default arguments including
i = 1:ncol(newresp), .combine = "cbind", .inorder = TRUE, .errorhandling = "remove", .packages = "spaMM".
control may be used to provide non-default values of these arguments. For example, .errorhandling = "pass" is useful to get error messages from the nodes, and therefore strongly recommended when first experimenting with this function.
for socket clusters, with doSNOW not attached, control$.packages is still handled. The result is still in the format returned by foreach with default .combine="cbind" or possible non-default .combine="rbind".
if a fork cluster is used, control$mc.silent can be used to control the mc.silent argument of mclapply.
A list of arguments passed to parallel::makeCluster. E.g., outfile="log.txt" may be useful to collect output from the nodes, and type="FORK" to force a fork cluster on linux(-alikes).
(for socket clusters only:) For debugging purposes. Effect, if any, is to be defined by the fn as provided by the user.
(all parallel contexts:) Integer, or NULL. If an integer, it is used as the iseed argument of clusterSetRNGStream to initialize "L'Ecuyer-CMRG" random-number generator (see Details). If iseed is NULL, the default generator is selected on each node, where its seed is not controlled.
(for socket clusters only:) Controls display of progress bar. See barstyle option for details.
(for socket clusters only:) A function to run on the cores before running fn. It may be used to check that all arguments of the fn can be evaluated in the cores' environments (the internal function .pretest_fn_on_cores provides an example).
Further arguments to be passed (unevaluated) to fn.
Control of random numbers through the "L'Ecuyer-CMRG" generator and the iseed argument is not sufficient for consistent results when the doSNOW parallel backend is used, so if you really need such control in a fn using random numbers, do not use doSNOW. Yet, it is fine to use doSNOW for bootstrap procedures in spaMM, because the fitting functions do not use random numbers: only sample simulation uses them, and it is not performed in parallel.
dofuture for an alternative implementation of (essentially) the same functionalities, and wrap_parallel for its differences from dopar.
## See source code of spaMM_boot()
if (FALSE) {
# Useless function, but requiring some argument beyond the first
foo <- function(y, somearg, ...) {
if ( is.null(somearg) || TRUE ) length(y)
}
# Whether FORK can be used depends on OS and whether Rstudio is used:
dopar(matrix(1,ncol=4,nrow=3), foo, fit_env=list(), somearg=NULL,
nb_cores=2, cluster_args=list(type="FORK"))
}
Run the code above in your browser using DataLab