R help - Aug 2012 - more efficient way to parallel

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)

}

	[[alternative HTML version deleted]]

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 <jimmycloud@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)
>
> }
>
>
	[[alternative HTML version deleted]]