I am having tremendous fortune using the foreach function in the foreach 
package sending work out to multiple cores in order to reduce computational 

I am experimenting with which types of tasks benefit from running in parallel 
and which do not and so this is a bit of a learning experience by trial and 

One particular task I cannot seem to realize a benefit from (in terms of 
reduced time) is splitting or subsetting a large data frame. I realize there 
are other "fast" options like using data.table, but current goal is to see if 
this can benefit from multiple cores or not. 

So, a very small toy example of how I am approaching the "traditional" and 
"parallel" way is as follows. My actual data is much, much larger and it turns 
out the parallel version of doing it this way vis-à-vis the traditional way is 
unbelievably slow. Hence Im not sure if there is a good theoretical reason why 
such a task cannot run faster when sent out to multiple cores if there is a 
user error that I need to better understand and correct


tmp <- data.frame(id = rep(1:200, each = 10), foo = rnorm(2000))

ff1 <- split(tmp, tmp$id)

myList <- unique(tmp$id)
N <- length(myList)
ff2 <- foreach(i = 1:N) %dopar% { tmp[which(tmp$id == myList[i]),]}


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