On Tue, 10 Apr 2007, yoooooo wrote:
>
> Hi all, I'm just curious how memory management works in R... I need to
run an
> optimization that keeps calling the same function with a large set of
> parameters... so then I start to wonder if it's better if I attach the
> variables first vs passing them in (coz that involves a lot of copying.. )
Your paranethetical comment is wrong: no copying is needed to 'pass in'
a
variable.
> Thus, I do this
> fn3 <- function(x, y, z, a, b, c){ sum(x, y, z, a, b, c) }
> fn4 <- function(){ sum(x, y, z, a, b, c) }
>
> rdn <- rep(1.1, times=1e8)
> r <- proc.time()
> for (i in 1:5)
> fn3(rdn, rdn, rdn, rdn, rdn, rdn)
> time1 <- proc.time() - r
> print(time1)
>
> lt <- list(x = rdn, y = rdn, z = rdn, a = rdn, b = rdn, c = rdn)
> attach(lt)
> r <- proc.time()
> for (i in 1:5)
> fn4()
> time2 <- proc.time() - r
> print(time2)
> detach("lt")
>
> The output is
> [1] 25.691 0.003 25.735 0.000 0.000
> [1] 25.822 0.005 25.860 0.000 0.000
>
> Turns out attaching takes longer to run.. which is counter intuitive
(unless
> the search to the pos=2 envir takes long time as well) Do you guys know why
> this is the case?
I would not trust timing differences of that nature: they often depend on
the state of the system, and in particular of the garbage collector.
You should be using system.time() for that reason: it calls the garbage
collector immediately before timing.
--
Brian D. Ripley, ripley at stats.ox.ac.uk
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UK Fax: +44 1865 272595