Dear all,
I am trying to parallelize the function npnewpar given below. When I am
comparing an application of "apply" with "parApply" the parallelized version
seems to be much slower (cf output below). Therefore I would like to ask how
the function could be parallelized more efficient. (With increasing sample size
the difference becomes smaller, but I was wondering about this big differences
and how it could be improved.)
Thank you very much for help in advance!
Best,
Martin
library(microbenchmark)
library(doParallel)
n <- 500
y <- rnorm(n)
Xc <- rnorm(n)
Xd <- sample(c(0,1), replace=TRUE)
Weights <- diag(n)
n1 <- 50
Xeval <- cbind(rnorm(n1), sample(c(0,1), n1, replace=TRUE))
detectCores()
cl <- makeCluster(4)
registerDoParallel(cl)
microbenchmark(apply(Xeval, 1, npnewpar, y=y, Xc=Xc, Xd = Xd, Weights=Weights,
h=0.5), parApply(cl, Xeval, 1, npnewpar, y=y, Xc=Xc, Xd = Xd, Weights=Weights,
h=0.5), times=100)
stopCluster(cl)
Unit: milliseconds
expr min lq mean median
apply(Xeval, 1, npnewpar, y = y, Xc = Xc, Xd = Xd, Weights = Weights,
h = 0.5) 4.674914 4.726463 5.455323 4.771016
parApply(cl, Xeval, 1, npnewpar, y = y, Xc = Xc, Xd = Xd, Weights = Weights,
h = 0.5) 34.168250 35.434829 56.553296 39.438899
uq max neval
4.843324 57.01519 100
49.777265 347.77887 100
npnewpar <- function(y, Xc, Xd, Weights, h, xeval) {
xc <- xeval[1]
xd <- xeval[2]
l <- function(x,X) {
w <- Weights[x,X]
return(w)
}
u <- (Xc-xc)/h
#K <- kernel(u)
K <- dnorm(u)
L <- l(xd,Xd)
nom <- sum(y*K*L)
denom <- sum(K*L)
ghat <- nom/denom
return(ghat)
}
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