On 26.02.2013 14:00, Alaios wrote:
Dear all,
I have a piece of code that I want to run in parallel (I am working in system
of 16 cores)
foreach (i=(seq(-93,-73,length.out=21))) %dopar%
{
threshold<-i
print(i)
do_analysis1(i,path)
do_analysis2(i,path)
do_something_else_analysis1(i,path)
something_else_now(i,path)
}
We do not know how your cluster was set up, hence cannot respond.
I'd just use the parallel (an R base package) and do:
library("parallel")
cl <- makeCluster(.....)
result <- parSapply(cl, seq(-93,-73,length.out=21), function(i){
threshold<-i
print(i)
do_analysis1(i,path)
do_analysis2(i,path)
do_something_else_analysis1(i,path)
something_else_now(i,path)
})
stopCluster(cl)
(untested, of course)
Uwe Ligges
as you can see I have already tried to make this run in parallel, meaning for
every i value each of the 16 processor shoule take a block of the body such
as:
threshold<-i
print(i)
do_analysis1(i,path)
do_analysis2(i,path)
do_something_else_analysis1(i,path)
something_else_now(i,,path)
and execute it . Unfortunately this does not work and oonly one processor looks
utilized.
Alternatively, mclapply have worked well in the past, but in this case I am not
sure how to convert the serial execution of the body of the loop to a list that
would be compatible with the mclapply.
I would like to thank you in advance for your help
Regards
Alex
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