doMC in R and foreach loop not working

Question

I am trying to get the foreach package for parallel processing in R working and I am having a couple of issues:

The doMC package that is required to make foreach work does not exist on CRAN for Windows. Some blogs suggest that doSNOW instead should do the same job. However, when I run the foreach command with doSNOW, %dopar% does not seem to work faster than %do%. In fact it is much slower. My CPU is an Intel i7 860 @ 2.80GHz with 8 GB of RAM. Below is my code:

##Run example in 1 core 
require(foreach)
require(doSNOW)
x= iris[which(iris[,5] != "setosa"),c(1,5)]
trials = 10000
system.time({
r= foreach(icount(trials), .combine=cbind) %do% {
ind=sample(100,100,replace=TRUE)
results1 = glm(x[ind,2]~x[ind,1],family=binomial(logit))
coefficients(results1)
}
})[3]
#  elapsed 
#  37.28 

# Same example in 2 cores
registerDoSNOW(makeCluster(2,type="SOCK"))
getDoParWorkers()
trials = 10000
system.time({
r= foreach(icount(trials), .combine=cbind) %dopar% {
ind=sample(100,100,replace=TRUE)
results1 = glm(x[ind,2]~x[ind,1],family=binomial(logit))
coefficients(results1)
}
})[3]
# elapsed 
#  108.14 

I re-installed all the packages required but still the same problems. Here is the output:

sessionInfo()

#R version 2.15.1 (2012-06-22) 
#Platform: i386-pc-mingw32/i386 (32-bit)

#locale:
#[1] LC_COLLATE=English_United States.1252 
#[2] LC_CTYPE=English_United States.1252   
#[3] LC_MONETARY=English_United States.1252
#[4] LC_NUMERIC=C                          
#[5] LC_TIME=English_United States.1252    

#attached base packages:
#[1] parallel  stats     graphics  grDevices datasets  utils     methods  
#[8] base     

#other attached packages:
#[1] doParallel_1.0.1 codetools_0.2-8  doSNOW_1.0.6     snow_0.3-10     
#[5] iterators_1.0.6  foreach_1.4.0    rcom_2.2-5       rscproxy_2.0-5  

#loaded via a namespace (and not attached):
#[1] compiler_2.15.1 tools_2.15.1   

Answer 1

You are better off in Windows to use doParallel():

require(foreach)
require(doParallel)
cl <- makeCluster(6) #use 6 cores, ie for an 8-core machine
registerDoParallel(cl)

Then run your foreach() %dopar% {}

EDIT: OP mentioned still seeing the problem, so including my exact code. Running on a 4-core Windows7 VM, R 2.15.1 32-bit, only allowing doParallel to use 3 of my cores:

require(foreach)
require(doParallel)
cl <- makeCluster(3)
registerDoParallel(cl)

x= iris[which(iris[,5] != "setosa"),c(1,5)]

trials = 1000 
system.time( 
  foreach(icount(trials), .combine=cbind) %do% 
  {  
    ind=sample(100,100,replace=TRUE) 
    results1 = glm(x[ind,2]~x[ind,1],family=binomial(logit)) 
    results1 = glm(x[ind,2]~x[ind,1],family=binomial(logit)) 
    results1 = glm(x[ind,2]~x[ind,1],family=binomial(logit)) 
    results1 = glm(x[ind,2]~x[ind,1],family=binomial(logit)) 
    coefficients(results1) 
  })[3] 

system.time( 
  foreach(icount(trials), .combine=cbind) %dopar% 
  {  
    ind=sample(100,100,replace=TRUE) 
    results1 = glm(x[ind,2]~x[ind,1],family=binomial(logit)) 
    results1 = glm(x[ind,2]~x[ind,1],family=binomial(logit)) 
    results1 = glm(x[ind,2]~x[ind,1],family=binomial(logit)) 
    results1 = glm(x[ind,2]~x[ind,1],family=binomial(logit)) 
    coefficients(results1) 
  })[3] 

In my case, I'm getting 17.6 sec for %do% and 14.8 sec for %dopar%. Watching the tasks execute, it appears that much of the execution time is the cbind, which is a common issue running parallel. In my own simulations, I have done custom work to save my detailed results as part of the parallel task rather than returning them through foreach, to remove that part of the overhead. YMMV.