[英]parallelize a nested for loop in R
I want to parallelize the below code in R. It's a nested for loop. 我想并行化R中的以下代码。这是一个嵌套的for循环。
for (i in 1:nrow(my_dataset_preprocessed)){
for (j in 1:ncol(my_dataset_preprocessed)){
my_dataset_preprocessed[i,j] = min( my_dataset_preprocessed[i,j], 0.1 )
}
}
I am trying the below code using doParallel 我正在尝试使用doParallel的以下代码
library(foreach)
library(doParallel)
registerDoParallel(detectCores())
clusterExport(cl, "my_dataset")
threshold_par <- function (X) {
co <- foreach(i=1:nrow(X)) %:%
foreach (j=1:ncol(X)) %dopar% {
co = min( X[i,j], 0.1 )
}
matrix(unlist(co), ncol=ncol(X))
}
system.time(threshold_par(my_dataset))
But I am getting the following error: 但是我收到以下错误:
Error in { : task 1 failed - "invalid 'type' (list) of argument" {中的错误:任务1失败-“参数的'类型'(列表)无效”
Is there any better way to parallelize this code (may be using parLapply)? 有没有更好的方法来并行化此代码(可能使用parLapply)? If not, how do I fix the above code? 如果没有,如何解决以上代码?
You didn't declare cl
. 您没有声明cl
。 The following worked if you remove clusterExport(cl, "my_dataset")
如果删除clusterExport(cl, "my_dataset")
以下方法会起作用
library(foreach)
library(doParallel)
registerDoParallel(detectCores())
getDoParWorkers()
# [1] 8
threshold_par <- function (X) {
co <- foreach(i=1:nrow(X)) %:%
foreach (j=1:ncol(X)) %dopar% {
co = min( X[i,j], 0.1 )
}
matrix(unlist(co), ncol=ncol(X))
}
test <- matrix(1:4, ncol=2)
system.time(threshold_par(test))
# user system elapsed
# 0.01 0.00 0.02
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