Dear Allan,
thank you very much for your answer.
If I got it right your idea is
a: create first all the i,j combinations and then
b. use mclapply (parallel version of lapply).
so I wrote some draft and run three experiments:
# code
require('multicore')
sr<-matrix(data=NA,ncol=256,nrow=256)
sum=0
i <- seq(from=-1,to=1-2/ncol(sr),length=ncol(sr))
j <- seq(from=-1,to=1-2/nrow(sr),length=nrow(sr))
iandj<-expand.grid(i=i,j=j)
system.time(for (i in seq(from=-1,to=1-2/ncol(sr),length=ncol(sr))){#Calculate
the estimated values, ncol*2 just to make sure all cels are #there
for (j in seq(from=-1,to=1-2/nrow(sr),length=nrow(sr))){
sum=i+j+sum
}
})
system.time(sum(unlist(lapply(1:nrow(iandj),function(rowId) { return
(iandj$i[rowId]+iandj$j[rowId]) }))))
system.time(sum(unlist(mclapply(1:nrow(iandj),function(rowId) { return
(iandj$i[rowId]+iandj$j[rowId]) }))))
# # # # Code end
Please feel free to copy and paste it. This will returns three times the
results of system.time for
a) normal for ...loop case
b) lapply
c) mclapply
In my normal four-core system I get the following results
a) user system elapsed
9.143 1.301 5.148
b) user system elapsed
3.482 0.534 1.993
c) user system elapsed
0.456 0.242 1.031
so far so good.
Then comes a parallel system with 32 cores that I have in work
This returns some strange results and I would like to ask from everyone to
comment
user system elapsed
a) 0.124 0.000 0.124
user system elapsed
b) 0.876 0.000 0.877
user system elapsed
c) 0.176 0.080 0.261
Why do you believe that a) performed much better (normal nested for loop) than
the 32 cores in parallel c)?
I would like to thank you in advance for your help
Best Regards
Alex
--- On Mon, 4/18/11, Allan Engelhardt <all...@cybaea.com> wrote:
> From: Allan Engelhardt <all...@cybaea.com>
> Subject: Re: [R] From nested loop to mclapply
> To: "Alaios" <ala...@yahoo.com>
> Cc: R-help@r-project.org
> Date: Monday, April 18, 2011, 5:36 PM
> Try help("expand.grid",
> package="base") for one way to create the
> combinations of (i,j) outside the loop, or perhaps
> vignette("nested",
> package="foreach") which does it "automatically" (rather:
> naturally).
>
> Allan
>
> On 18/04/11 16:53, Alaios wrote:
> > Dear all,
> > I am trying to find a decent way to speed up my code.
> >
> > So far I have used mclapply with really good results
> (parallel version of lapply). I have a nested loop that I
> would like to help me convert it to lapply
> >
> > for (i in
> seq(from=-1,to=1-2/ncol(sr),length=ncol(sr))){
> > for (j in
> seq(from=-1,to=1-2/nrow(sr),length=nrow(sr))){
> >
> estimatedsr[findCoord(c(i,j),sr)[1],findCoord(c(i,j),sr)[2]
> ]<-fxy(c(i,j))
> > }
> >
> > So far I have converted some one-depth for loops to
> lapply but I am not sure If I can use lapply to convert a
> nested loop to something simpler.
> >
> > Best Regards
> > Alex
> >
> > ______________________________________________
> > R-help@r-project.org
> mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained,
> reproducible code.
>
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