Parallel Computing in R

parallel.R

library(parallel)
library(BGLR)
library(microbenchmark)

data(mice)
X <- mice.X
y <- mice.pheno$Obesity.BMI

GWAS <- function (i) {
  summary(lm(y ~ X[, i]))$coef[2, ]
}

seq1 <- function () {
  out <- matrix(nrow = ncol(X), ncol = 4, NA)
  for (i in 1:ncol(X)) {
    out[i, ] <- GWAS(i)
  }
  out
}

seq2 <- function () {
  t(sapply(1:ncol(X), GWAS))
}

par1 <- function () {
  cl <- makeCluster(detectCores())
  clusterExport(cl, c('y', 'X'))
  out <- t(parSapply(cl, 1:ncol(X), GWAS))
  stopCluster(cl)
  out
}

par2 <- function () {
  t(simplify2array(mclapply(1:ncol(X), GWAS, mc.cores = detectCores())))
}

microbenchmark(
  seq1(),
  seq2(),
  par1(),
  par2(),
  times = 5
)