[R] R parallel - slow speed

[Martin Spindler]

Thank you very much for your help.
 
I tried it under Unix and then the parallel version was faster than under Windows (but still slower than the non parall version). This is an important point to keep in mind. Thanks for this.
 
Best,
 
Martin

 
 

Gesendet: Donnerstag, 30. Juli 2015 um 14:56 Uhr
Von: "Jeff Newmiller" <jdnewmil at dcn.davis.CA.us>
An: "Martin Spindler" <Martin.Spindler at gmx.de>, "r-help at r-project.org" <r-help at r-project.org>
Betreff: Re: [R] R parallel - slow speed
Parallelizing comes at a price... and there is no guarantee that you can afford it. Vectorizing your algorithms is often a better approach. Microbenchmarking is usually overkill for evaluating parallelizing.

You assume 4 cores... but many CPUs have 2 cores and use hyperthreading to make each core look like two.

The operating system can make a difference also... Windows processes are more expensive to start and communicate between than *nix processes are. In particular, Windows seems to require duplicated RAM pages while *nix can share process RAM (at least until they are written to) so you end up needing more memory and disk paging of virtual memory becomes more likely.
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On July 30, 2015 8:26:34 AM EDT, Martin Spindler <Martin.Spindler at gmx.de> wrote:
>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
>
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>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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