> On Dec 1, 2016, at 9:27 AM, Doran, Harold <hdo...@air.org> wrote:
>
> 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$id == myList[i]),]}
I would have imagined that using split to deliver separate instance of separate
data.frame parcels to the `i` -argument would be more sensible. Otherwise you
are sending full copies to each worker and then doing the extraction N times
rather than once.There's a lot of checking using data.frame methods. I also
think you would want to avoid making reference to objects "outside" the
parallel function application.
ff2 <- foreach( z = iter( ff1) ) %dopar% { max(z$id) }
>
> Thanks,
> Harold
>
> ______________________________________________
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David Winsemius
Alameda, CA, USA
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