I am having tremendous fortune using the foreach function in the foreach
package sending work out to multiple cores in order to reduce computational
time.
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
error.
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
library(foreach)
library(doParallel)
registerDoParallel(cores=4)
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]),]}
Thanks,
Harold
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