[R] R parallel - slow speed

[jim holtman]

I ran a test on my Windows box with 4 CPUs.  THere were 4 RScript processes
started in response to the request for a cluster of 4.  Each of these ran
for an elapsed time of around 23 seconds, making the median time around 0.2
seconds for 100 iterations as reported by microbenchmark.  The 'apply' only
takes about 0.003 seconds for a single iteration - again what
microbenchmark is reporting.

The 4 RScript processes each use about 3 CPU seconds in the 23 seconds of
elapsed time, most of that is probably the communication and startup time
for the processes and reporting results.

So as was pointed out previous there is overhead is running in parallel.
You probably have to have at least several seconds of heavy computation for
a iteration to make trying to parallelize something.  You should also
investigate exactly what is happening on your system so that you can
account for the time being spent.


Jim Holtman
Data Munger Guru

What is the problem that you are trying to solve?
Tell me what you want to do, not how you want to do it.

On Thu, Jul 30, 2015 at 8:56 AM, Jeff Newmiller <jdnewmil at dcn.davis.ca.us>
wrote:

> 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.
> ---------------------------------------------------------------------------
> Jeff Newmiller                        The     .....       .....  Go Live...
> DCN:<jdnewmil at dcn.davis.ca.us>        Basics: ##.#.       ##.#.  Live
> Go...
>                                       Live:   OO#.. Dead: OO#..  Playing
> Research Engineer (Solar/Batteries            O.O#.       #.O#.  with
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> ---------------------------------------------------------------------------
> Sent from my phone. Please excuse my brevity.
>
> 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
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >
> >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)
> >}
> >
> >______________________________________________
> >R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> >https://stat.ethz.ch/mailman/listinfo/r-help
> >PLEASE do read the posting guide
> >http://www.R-project.org/posting-guide.html
> >and provide commented, minimal, self-contained, reproducible code.
>
> ______________________________________________
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>

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