[R] Timings of function execution in R [was Re: R in Industry]

[Prof Brian Ripley]

> x <- rnorm(10000)
> system.time(for(i in 1:1000) pmax(x, 0))
    user  system elapsed
    4.43    0.05    4.54
> pmax2 <- function(k,x) (x+k + abs(x-k))/2
> system.time(for(i in 1:1000) pmax2(x, 0))
    user  system elapsed
    0.64    0.03    0.67
> pm <- function(x) {z <- x<0; x[z] <- 0; x}
> system.time(for(i in 1:1000) pm(x))
    user  system elapsed
    0.59    0.00    0.59
> system.time(for(i in 1:1000) pmax.int(x, 0))
    user  system elapsed
    0.36    0.00    0.36

So at least on one system Thomas' solution is a little faster, but a 
C-level n-args solution handling NAs is quite a lot faster.

On Fri, 9 Feb 2007, Martin Maechler wrote:

>>>>>> "TL" == Thomas Lumley <tlumley at u.washington.edu>
>>>>>>     on Fri, 9 Feb 2007 08:13:54 -0800 (PST) writes:
>
>    TL> On 2/9/07, Prof Brian Ripley <ripley at stats.ox.ac.uk> wrote:
>    >>> The other reason why pmin/pmax are preferable to your functions is that
>    >>> they are fully generic.  It is not easy to write C code which takes into
>    >>> account that <, [, [<- and is.na are all generic.  That is not to say that
>    >>> it is not worth having faster restricted alternatives, as indeed we do
>    >>> with rep.int and seq.int.
>    >>>
>    >>> Anything that uses arithmetic is making strong assumptions about the
>    >>> inputs.  It ought to be possible to write a fast C version that worked for
>    >>> atomic vectors (logical, integer, real and character), but is there
>    >>> any evidence of profiled real problems where speed is an issue?
>
>
>    TL> I had an example just last month of an MCMC calculation where profiling showed that pmax(x,0) was taking about 30% of the total time.  I used
>
>    TL> function(x) {z <- x<0; x[z] <- 0; x}
>
>    TL> which was significantly faster. I didn't try the
>    TL> arithmetic solution.
>
> I did - eons ago as mentioned in my message earlier in this
> thread. I can assure you that those (also mentioned)
>
>  pmin2 <- function(k,x) (x+k - abs(x-k))/2
>  pmax2 <- function(k,x) (x+k + abs(x-k))/2
>
> are faster still, particularly if you hardcode the special case of k=0!
> {that's how I came about these:  pmax(x,0) is also denoted  x_+, and
> 	x_+ := (x + |x|)/2
> 	x_- := (x - |x|)/2
> }
>
>    TL> Also, I didn't check if a solution like this would still
>    TL> be faster when both arguments are vectors (but there was
>    TL> a recent mailing list thread where someone else did).
>
> indeed, and they are faster.
> Martin
>

-- 
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