The “parallel” library is used for parallel processing!<\/p>\n
Uses an internal R dataset called MASS which has ‘Boston’ and ‘iris’ data.<\/p>\n
This can be used in a batch script on the RACC. 'R script for parallelising.<\/i><\/p>\n library(parallel) starts <- rep(100, 40)<\/p>\n fx <- function(nstart) kmeans(Boston, 4, nstart=nstart) system.time( system.time( x <- iris[which(iris[,5] != \"setosa\"), c(1,5)] boot_fx <- function(trial) { <\/code><\/p>\n Output shows the processing speeds using the different libraries lapply and mclapply<\/p>\n","protected":false},"excerpt":{"rendered":" The “parallel” library is used for parallel processing! Uses an internal R dataset called MASS which has ‘Boston’ and ‘iris’ data. This can be used in a batch script on the RACC. \u200b ‘R script for parallelising. library(parallel) library(MASS) <\/p>\n","protected":false},"author":7,"featured_media":5292,"template":"","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"__cvm_playback_settings":[],"__cvm_video_id":"","_links_to":"","_links_to_target":""},"kbe_taxonomy":[61],"kbe_tags":[76,74,75],"class_list":["post-5290","kbe_knowledgebase","type-kbe_knowledgebase","status-publish","has-post-thumbnail","hentry","kbe_taxonomy-academic-cluster-articles","kbe_tags-batch-processing","kbe_tags-r","kbe_tags-rstudio"],"acf":[],"yoast_head":"\n
\n\u200b
\n<\/p>\n
\nlibrary(MASS)<\/p>\n
\nnumCores <- detectCores()
\nnumCores<\/p>\n
\n results <- lapply(starts, fx)
\n)<\/p>\n
\n results <- mclapply(starts, fx, mc.cores = numCores)
\n)<\/p>\n
\ntrials <- seq(1, 10000)<\/p>\n
\n ind <- sample(100, 100, replace=TRUE)
\n result1 <- glm(x[ind,2]~x[ind,1], family=binomial(logit))
\n r <- coefficients(result1)
\n res <- rbind(data.frame(), r)
\n}
\nsystem.time({
\n results <- mclapply(trials, boot_fx, mc.cores = numCores)
\n})<\/p>\n