Tutorial by Examples

The foreach package brings the power of parallel processing to R. But before you want to use multi core CPUs you have to assign a multi core cluster. The doSNOW package is one possibility. A simple use of the foreach loop is to calculate the sum of the square root and the square of all numbers from...

The base package parallel allows parallel computation through forking, sockets, and random-number generation. Detect the number of cores present on the localhost: parallel::detectCores(all.tests = FALSE, logical = TRUE) Create a cluster of the cores on the localhost: parallelCluster <- para...

A major problem with parallelization is the used of RNG as seeds. Random numbers by the number are iterated by the number of operations from either the start of the session or the most recent set.seed(). Since parallel processes arise from the same function, it can use the same seed, possibly causin...

The mcparallelDo package allows for the evaluation of R code asynchronously on Unix-alike (e.g. Linux and MacOSX) operating systems. The underlying philosophy of the package is aligned with the needs of exploratory data analysis rather than coding. For coding asynchrony, consider the future packag...