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September 10, 2015 15:20
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Parallel Computing in R
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| 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 | |
| ) |
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