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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