当串行执行随机森林时,它在我的系统上使用8GB的RAM,当并行执行它时,它使用超过两倍的RAM(18GB).如果并行执行此操作,如何将其保持在8GB？这是代码：
install.packages('foreach')
install.packages('doSMP')
install.packages('randomForest')
library('foreach')
library('doSMP')
library('randomForest')
NbrOfCores <- 8
workers <- startWorkers(NbrOfCores) # number of cores
registerDoSMP(workers)
getDoParName() # check name of parallel backend
getDoParVersion() # check version of parallel backend
getDoParWorkers() # check number of workers
#creating data and setting options for random forests
#if your run this please adapt it so it won't crash your system! This amount of data uses up to 18GB of RAM.
x <- matrix(runif(500000),100000)
y <- gl(2,50000)
#options
set.seed(1)
ntree=1000
ntree2 <- ntree/NbrOfCores
gc()
#running serialized version of random forests
system.time(
rf1 <- randomForest(x,y,ntree = ntree))
gc()
#running parallel version of random forests
system.time(
rf2 <- foreach(ntree = rep(ntree2,8),.combine = combine,.packages = "randomForest") %dopar% randomForest(x,ntree = ntree))
