On Thu, 27 Jan 2005, Paul Gilbert wrote:
A few weeks ago I noticed
z <- matrix(rnorm(20000),10000,2)
system.time(for (i in 1:1000) apply(z,2,sum))[1] 13.44 0.48 14.08 0.00 0.00
system.time(for (i in 1:1000) rep(1,10000) %*% z)[1] 6.46 0.11 6.84 0.00 0.00
So both run in a few milliseconds for rather large problems.
which seemed completely contrary to all my childhood teachings. Now
system.time(for (i in 1:1000) crossprod(rep(1,10000), z))[1] 1.90 0.12 2.24 0.00 0.00
makes sense because it is suppose to be faster than %*% , but why is apply so slow?
`so slow' sic: what are you going to do in the 7ms you saved?
(And should I go back and change apply in my code everywhere or is this likely to reverse again?)
It's not likely. apply is an R-level loop, and %*% is a C-level one.
However, %*% is not supposed to be much slower than crossprod, and the devil is in the details of how the BLAS is implemented: the code is very similar.
That %*% was faster than apply has been true in all my (17 years) of S/R experience. Your childhood may predate S3, of course.
I still think one should use row/colSums for clarity with acceptable
efficiency. It must be very unusual for these operations to be a dominant part of a calculation, so let's not lose proportion here.
Paul Gilbert
Patrick Burns wrote:
The following is at least as much out of intellectual curiosity as for practical reasons. On reviewing some code written by novices to R, I came across:
crossprod(x, y)[1,1]
I thought, "That isn't a very S way of saying that, I wonder what the penalty is for using 'crossprod'." To my surprise the penalty was substantially negative. Handily the client had S-PLUS as well -- there the sign of the penalty was as I had expected, but the order of magnitude was off.
Here are the timings of 1 million computations on vectors of length 1000. This is under Windows, R version 1.9.1 and S-PLUS 6.2 (on the same machine).
Command R S-PLUS sum(x * y) 28.61 97.6 crossprod(x, y)[1,1] 6.77 2256.2
Another example is when computing the sums of the columns of a matrix. For example:
set.seed(1) jjm <- matrix(rnorm(600), 5)
Timings for this under Windows 2000 with R version 2.0.1 (on an old chip running at about 0.7Ghz) for 100,000 computations are:
apply(jjm, 2, sum) 536.59 colSums(jjm) 18.26 rep(1,5) %*% jjm 15.41 crossprod(rep(1,5), jjm) 13.16
(These timings seem to be stable across R versions and on at least one Linux platform.)
Andy Liaw showed another example of 'crossprod' being fast a couple days ago on R-help.
Questions for those with a more global picture of the code:
* Is the speed advantage of 'crossprod' inherent, or is it because more care has been taken with its implementation than the other functions?
* Is 'crossprod' faster than 'sum(x * y)' because 'crossprod' is going to BLAS while 'sum' can't?
* Would it make sense to (essentially) use 'crossprod' in 'colSums' and its friends at least for the special case of matrices?
Patrick Burns
Burns Statistics [EMAIL PROTECTED] +44 (0)20 8525 0696 http://www.burns-stat.com (home of S Poetry and "A Guide for the Unwilling S User")
______________________________________________ R-devel@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
______________________________________________ R-devel@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
-- Brian D. Ripley, [EMAIL PROTECTED] 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
______________________________________________ R-devel@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-devel
