R 并行处理错误 `Error in checkForRemoteErrors(val) : 6 nodes produced errors; first error: subscript out of bounds`

Question

我正在学习并行处理，以此来处理一些庞大的数据集。

我有一些预定义的变量如下:

CV <- function(mean, sd) {(sd / mean) * 100} 
distThreshold <- 5 # Distance threshold 
CVThreshold <- 20 # CV threshold 

LocalCV <- list()
Num.CV <- list()

然后加载parallel库，将基变量和库分配给集群:

library(parallel)
clust_cores <- makeCluster(detectCores(logical = T) ) 
clusterExport(clust_cores, c("i","YieldData2rd","CV", "distThreshold", "CVThreshold"))
clusterEvalQ(clust_cores, library(sp))

然后传递集群参数clust_cores至 parSapply :

for (i in seq(YieldData2rd)) {
  LocalCV[[i]] = parSapply(clust_cores, X = 1:length(YieldData2rd[[i]]), 
                   FUN = function(pt) {
                     d = spDistsN1(YieldData2rd[[i]], YieldData2rd[[i]][pt,])
                     ret = CV(mean = mean(YieldData2rd[[i]][d < distThreshold, ]$yield), 
                              sd = sd(YieldData2rd[[i]][d < distThreshold, ]$yield))
                     return(ret)
                   }) # calculate CV in the local neighbour 
}

stopCluster(clust_cores) 

然后我得到 Error in checkForRemoteErrors(val) : 6 nodes produced errors; first error: subscript out of bounds除了warning messages:
1: closing unused connection (<-localhost:11688) .

请告诉我如何解决这个问题。

为了可重现的示例，我创建了一个大列表对象，它在原始 for 中运行良好没有并行处理组件的循环。

library('rgdal')

Yield1 <- data.frame(yield=rnorm(460, mean = 10), x1=rnorm(460, mean = 1843235), x2=rnorm(460,mean = 5802532))
Yield2 <- data.frame(yield=rnorm(408, mean = 10), x1=rnorm(408, mean = 1843235), x2=rnorm(408, mean = 5802532))
Yield3 <- data.frame(yield=rnorm(369, mean = 10), x1=rnorm(369, mean = 1843235), x2=rnorm(369, mean = 5802532))

coordinates(Yield1) <- c('x1', 'x2')
coordinates(Yield2) <- c('x1', 'x2')
coordinates(Yield3) <- c('x1', 'x2')

YieldData2rd <- list(Yield1, Yield2, Yield3)

Answer 1

感谢@Omry Atia 的评论，我开始研究 foreach 包并进行了第一次尝试。

library(foreach)
library(doParallel)

#setup parallel backend to use many processors
cores=detectCores()
clust_cores <- makeCluster(cores[1]-1) #not to overload your computer
registerDoParallel(clust_cores)

LocalCV = foreach(i = seq(YieldData2rd), .combine=list, .multicombine=TRUE) %dopar% {
                       LocalCV[[i]] = sapply(X = 1:length(YieldData2rd[[i]]), 
                                            FUN = function(pt) {
                                                  d = spDistsN1(YieldData2rd[[i]], YieldData2rd[[i]][pt,])
                                                ret = CV(mean = mean(YieldData2rd[[i]][d < distThreshold, ]$yield), 
                                                 sd = sd(YieldData2rd[[i]][d < distThreshold, ]$yield))
                                                 return(ret)
                                                 }) # calculate CV in the local neighbour 
                       }

stopCluster(clust_cores)

无需将 LocalCV 放在 foreach 的前面，它就会打印出全部内容。

它将在一些巨大的数据集上尝试新代码，看看它能达到多快。

引用:run a for loop in parallel in R