Parallel proccessing in R doParallel foreach

Question

I have written a script that runs fine, but it doesn't not appear to be doing the parallel processing. I tried changing the cores from 3 to 16 but the speed at which the data was being generated did not change. Can anyone let me know what I am doing wrong and how I can get this to work?

setwd("E:/Infections")

if (!require("pacman")) install.packages("pacman")
pacman::p_load(lakemorpho,rgdal,maptools,sp,doParallel,foreach,
doParallel)

cl <- makeCluster(5, outfile="E:/Infections/debug.txt")
registerDoParallel(cl)
x<-readOGR("E:/Infections/ByHUC6","Kodiak")
x_lake_length<-vector("numeric",length = nrow(x))
for(i in 1:nrow(x)){
  tmp<-lakeMorphoClass(x[i,],NULL,NULL,NULL)
  x_lake_length[i]<-lakeMaxLength(tmp,200)
  print(i)
  Sys.sleep(0.1)
}
df_Kodiak <- data.frame(x_lake_length)
write.table(df_Kodiak,file="E:/Infections/ByHUC6/Kodiak.csv",row.names=TRUE,col.names=TRUE, sep=",")

Answer 1

Alright, I think I got it by invoking foreach and %dopar%:

# Libraries ---------------------------------------------------------------
if (!require("pacman")) install.packages("pacman")
pacman::p_load(lakemorpho,rgdal,maptools,sp,doParallel,foreach,
               doParallel)

# Data --------------------------------------------------------------------
ogrDrivers()
dsn <- system.file("vectors", package = "rgdal")[1]
ogrListLayers(dsn)
ogrInfo(dsn=dsn, layer="trin_inca_pl03")
owd <- getwd()
setwd(dsn)
ogrInfo(dsn="trin_inca_pl03.shp", layer="trin_inca_pl03")
setwd(owd)
x <- readOGR(dsn=dsn, layer="trin_inca_pl03")
summary(x)

# HPC ---------------------------------------------------------------------
cores_2_use <- detectCores() - 4
cl          <- makeCluster(cores_2_use, useXDR = F)
clusterSetRNGStream(cl, 9956)
registerDoParallel(cl, cores_2_use)

# Analysis ----------------------------------------------------------------  
myfun <- function(x,i){tmp<-lakeMorphoClass(x[i,],NULL,NULL,NULL)
                        x_lake_length<-vector("numeric",length = nrow(x))
                        x_lake_length[i]<-lakeMaxLength(tmp,200)
                        print(i)
                        Sys.sleep(0.1)}

foreach(i = 1:nrow(x),.combine=cbind,.packages=c("lakemorpho","rgdal"))  %dopar% (
  myfun(x,i)
)

df_Kodiak <- data.frame(x_lake_length)

As you can see in the screenshot below this creates an army of Rscript.exe processes using 20 of 24 CPU cores. Of course, the example data I used is small so it didn't really need all those cores, but it should serve as a proof of concept.

I never go above that ratio because if you use 100% of all CPU cores sometimes bad things happen and other server users may not be happy with you.