R help - Dec 2016 - Splitting or Subsetting Using foreach

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-?-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

> On Dec 1, 2016, at 9:27 AM, Doran, Harold <HDoran at 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-?-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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