Please, you can't compare calls starting with different memory settings
(and the garbage collector does adjust to recent usage). Try
them in the other order or in parallel sessions.
>From the times you quote it looks to me as if you would benefit from using
an optimized BLAS. Here are some results on a dual core 64-bit Linux
system with the Goto BLAS and one thread, starting from the same point (a
fresh session loading a saved workspace containing AA and BB)
> system.time(t(AA)%*%BB,gcFirst=TRUE)
user system elapsed
4.304 0.046 4.351
> system.time(t(AA)%*%BB,gcFirst=TRUE)
user system elapsed
5.026 0.053 5.082
> system.time(t(AA)%*%BB,gcFirst=TRUE)
user system elapsed
4.992 0.033 5.028
> system.time(t(AA)%*%BB,gcFirst=TRUE)
user system elapsed
4.979 0.043 5.026
> system.time(t(AA)%*%BB,gcFirst=TRUE)
user system elapsed
5.012 0.030 5.046
and in another session
> system.time(crossprod(AA,BB),gcFirst=TRUE)
user system elapsed
4.616 0.026 4.644
> system.time(crossprod(AA,BB),gcFirst=TRUE)
user system elapsed
4.791 0.000 4.817
> system.time(crossprod(AA,BB),gcFirst=TRUE)
user system elapsed
4.764 0.000 4.779
> system.time(crossprod(AA,BB),gcFirst=TRUE)
user system elapsed
4.092 0.000 4.095
> system.time(crossprod(AA,BB),gcFirst=TRUE)
user system elapsed
4.824 0.000 4.834
Note how there is a lot of variability depending on the initial memory
state. I would be surprised if the differences you are seeing exceed the
reproducibilty spreads.
BTW: gcFirst=TRUE is the default, and I get a considerable speed up (but
even more variability) by allowing multiple threads.
On Mon, 10 Mar 2008, Ole Fredslund Christensen wrote:
> Dear Rdevelopers
>
> The background for this email is that I was helping a PhD student to
> improve the speed of her R code. I suggested to replace calls like
> t(AA)%*% BB by crossprod(AA,BB) since I expected this to be faster. The
> surprising result to me was that this change actually made her code
> slower.
>
>
>> ## Examples :
>>
>> AA <- matrix(rnorm(3000*1000),3000,1000)
>> BB <- matrix(rnorm(3000^2),3000,3000)
>> system.time(crossprod(AA,BB),gcFirst=TRUE)
> user system elapsed
> 24.58 0.06 24.69
>> system.time(t(AA)%*%BB,gcFirst=TRUE)
> user system elapsed
> 23.25 0.04 23.32
>>
>>
>> AA <- matrix(rnorm(2000^2),2000,2000)
>> BB <- matrix(rnorm(2000^2),2000,2000)
>> system.time(crossprod(AA,BB),gcFirst=TRUE)
> user system elapsed
> 21.94 0.03 21.98
>> system.time(t(AA)%*%BB,gcFirst=TRUE)
> user system elapsed
> 21.16 0.02 21.19
>>
>>
>> version
> _
> platform i386-pc-mingw32
> arch i386
> os mingw32
> system i386, mingw32
> status
> major 2
> minor 6.2
> year 2008
> month 02
> day 08
> svn rev 44383
> language R
> version.string R version 2.6.2 (2008-02-08)
>
>
> Clearly there are many examples where crossprod is indeed faster than
> t(x)%*%y,
> but I suggest to change the wording in the help file for crossprod such
> that it says
> ".... formally equivalent (but often faster than) the call t(x)%*%y ...
> ".
>
>
> Yours
>
> Ole Christensen
>
>
>
>
>
> [[alternative HTML version deleted]]
>
> ______________________________________________
> R-devel@r-project.org 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
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