matemopa.blogg.se

# "getDefaultCluster" "makeCluster" "makeForkCluster"

# "clusterSetRNGStream" "clusterSplit" "detectCores" ls("package:parallel") # "clusterApply" "clusterApplyLB" "clusterCall" Let’s list all the function for each package. These packages do not have an extensive amount of functions compared to tidyverse. Let’s load these packages in our environment for (package in c("parallel","doParallel","foreach")) # Loading required package: foreach # Loading required package: iterators There are three packages you have to know to do parallel computing in R. If we could utilise four cores to calculate a subset of the dataset, a quarter each, and add the four subtotals in the end, we could have a much faster outcome. For example the function sum() runs will process the whole dataset in a single core. As a default, R runs serially, it runs only one one core / thread.