Nothing
# These functions are taken fromthe Rdsm package by N. Matloff, with unneeded parts
# removed.
# To avoid getting notes about global variables in CRAN check
if(getRversion() >= "2.15.1") utils::globalVariables("myinfo")
#' @keywords internal
getidxs <- function(m) {
splitIndices(m,myinfo$nwrkrs)[[myinfo$id]]
}
#' @keywords internal
mgrinit <- function(cls) {
# set up so that each worker node will have a global variable myinfo
# that contains the thread ID and number of threads
setmyinfo <- function(i,n) {
assign("myinfo",list(id = i,nwrkrs = n),pos=tmpenv)
}
ncls <- length(cls)
clusterEvalQ(cls,tmpenv <- new.env())
clusterApply(cls,1:ncls,setmyinfo,ncls)
clusterEvalQ(cls,myinfo <- get("myinfo",tmpenv))
# we create global variables only at the workers, thus OK for CRAN,
# but CRAN check complains anyway, so here is a workaround
clusterEvalQ(cls,gbl <- globalenv())
# send the threads needed Rdsm functions
clusterExport(cls,"getidxs",envir=environment())
}
#' @keywords internal
mgrmakevar <- function(cls,varname,nr,nc,vartype="double") {
tmp <- big.matrix(nrow=nr,ncol=nc,type=vartype)
# make accessible to manager
assign(varname,tmp,pos=parent.frame())
# get the descriptor for this big.matrix object, to send to the
# worker nodes
clusterExport(cls,"varname",envir=environment())
desc <- describe(tmp)
clusterExport(cls,"desc",envir=environment())
clusterEvalQ(cls, tmp <- attach.big.matrix(desc))
clusterEvalQ(cls,assign(varname,tmp))
invisible(0)
}
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