Not that I've had a chance to really look at the problem, but I've removed
outer loops using parLapply from the parallel package. Works great.
On Mon, Aug 6, 2012 at 11:41 AM, Jie <jimmycl...@gmail.com> wrote:
> After searching online, I found that clusterCall or foreach might be the
> solution.
>
> Best wishes,
> Jie
>
> On Sun, Aug 5, 2012 at 10:23 PM, Jie <jimmycl...@gmail.com> wrote:
>
> > Dear All,
> >
> > Suppose I have a program as below: Outside is a loop for simulation (with
> > random generated data), inside there are several sapply()'s (10~100) over
> > the data and something else, but these sapply's have to be sequential.
> And
> > each sapply do not involve very intensive calculation (a few seconds
> only).
> > So the outside loop takes minutes to finish one iteration.
> > I guess the better way is not to parallel sapply but the outer loop.
> > But I have no idea how to modify it. I have a simple code here. Only two
> > sapply's involved for simplicity. The logical in the sapply is not
> > important.
> > Thank you for your attention and suggestion.
> >
> > library(parallel)
> > library(MASS)
> > result.seq=c()
> > Maxi <- 100
> > for (i in 1:Maxi)
> > {
> > ## initialization, not of interest
> > Sigmahalf <- matrix(sample(1:10000,size = 10000,replace =T ), 100)
> > Sigma <- t(Sigmahalf)%*%Sigmahalf
> > x <- mvrnorm(n=1000, rep(0, 10), Sigma)
> > xlist <- list()
> > for (j in 1:1000)
> > {
> > xlist[[j]] <- list(X = matrix( x [j, ],5))
> > }
> > ## end of initialization
> >
> > dd1 <- sapply(xlist,function(s) {min(abs((eigen(s$X))$values))})
> > ##
> > sumdd1=sum(dd1)
> > for (j in 1:1000)
> > {
> > xlist[[j]]$dd1 <- dd1[j]/sumdd1
> > }
> > ## Assume dd2 and dd1 can not be combined in one sapply()
> > dd2 <- sapply(xlist, function(s){min(abs((eigen(s$X))$values))+s$dd1})
> > result.seq[i] <- sum(dd1*dd2)
> >
> > }
> >
> >
>
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>
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