Thanks for your reply! I just added some more details below.

Our code needs around 1GB of RAM and all machines and R configurations have
its default maximum above this number.

Our suspicion is that the windows server could run the code in half of its
current time (given the apparent factor of 2 between windows and other OS
timing). There may be something very important either in the R configuration
or in our code that we should take care of?

I appreciate a lot any further advice or hints, specially about speeding up
the code in the windows xp server with QuadCore Xeon processors.

Best regards,

Carlos




================

**Server running WinXP 64bit (R 2.10.0 32bit , QuadCore Xeon 2.6GHz 8G Ram)
Time per 25 Iterations "6.17"

--------
**Dell Latitude running Linux 32bit (R 2.9.2, Intel Core 2 Duo P9500 @
2.53GHz, 4GB ram)
Time per 25 iterations "2.88"

**Dell Latitude running Win Vista 32bit (R 2.10.0, Intel Core 2 Duo P9500 @
2.53GHz, 4GB ram) with New DLL in terminal
Time per 25 iterations "5.53"

-------
**Macbook pro running Snow Leopard (R 2.10.0, 2.16GHz Intel Core 2 Duo & 2GB
ram)
Time per 25 Iterations "4.58"  (both R 2.10.0 32bit and 64bit produce almost
identical timings)

**Macbook pro running WinXp natively (R 2.10.0 32bit, 2.16GHz Intel Core 2
Duo & 2GB ram)
Time per 25 Iterations "8.23"

note: for the Dell and MacBook Pro we replaced the Rblas.dll file of R for
Windows with the file available here
http://cran.r-project.org/bin/windows/contrib/ATLAS/C2D/

==============



On Thu, Nov 19, 2009 at 5:06 PM, Marc Schwartz <marc_schwartz@me.com> wrote:

> On Nov 19, 2009, at 9:25 AM, Carlos Hernandez wrote:
>
>  Dear All,
>> I appreciate any advice or hints you could provide about the following.
>>
>> We are running R code in a server (running Windows XP and QuadCore Xeon
>> processors, see details below) and we would like to use the server
>> efficiently. Our code takes a bit more than 6 seconds per 25 iterations in
>> the server using a default R 2.10.0 installation.
>>
>> We tested our code in two other computers, a Dell Latitute and a MacBook
>> Pro, and from the details that i include below you will notice that the
>> code
>> needs almost twice the time when we used R for Windows compared against
>> the
>> time the code needs when we use Linux or MacOSX 10.6.2 in each of these
>> computers.
>>
>> I'm sorry I don't provide details on the code we are using. The code
>> consists of all sort of operations (matrix inverses, random number
>> generation, vectorized functions, a few loops, and so on). I hope I can
>> get
>> some advice from you despite the lack of specific code details.
>>
>> Is there any important R feature we should configure manually in the
>> windows
>> server to speed the code up? Is there an optimized BLAS available
>> somewhere
>> for this type of machine? Is these something else apart of an optimized
>> BLAS
>> that we could do to improve the timing?
>>
>> Best regards,
>>
>> Carlos
>>
>>
>>
>>
>> **Server running WinXP (QuadCore Xeon 2.6GHz 8G Ram)
>> Time per 25 Iterations "6.17"
>>
>> --------
>> **Dell Latitude running Linux (R 2.9.2, Intel Core 2 Duo P9500 @ 2.53GHz,
>> 4GB ram)
>> Time per 25 iterations "2.88"
>>
>> **Dell Latitude running Win Vista (R 2.10.0, Intel Core 2 Duo P9500 @
>> 2.53GHz, 4GB ram) with New DLL in terminal
>> Time per 25 iterations "5.53"
>>
>> -------
>> **Macbook pro (2.16GHz Intel Core 2 Duo & 2GB ram)
>> Time per 25 Iterations "4.58"
>>
>> **Macbook pro running WinXp (2.16GHz Intel Core 2 Duo & 2GB ram)
>> Time per 25 Iterations "8.23"
>>
>>
>> note: for the Dell and MacBook Pro we replaced the Rblas.dll file of R for
>> Windows with the file available here
>> http://cran.r-project.org/bin/windows/contrib/ATLAS/C2D/
>>
>
>
> Are you running 32 bit R on each platform or are you using 64 bit R on
> Linux and OSX?
>
> On the Dell, you are running two different versions of R and you don't
> indicate the R versions on the MacBook.
>
> The RAM configuration on each computer is different, which will impact the
> timings to some extent, depending upon how much RAM you may require for your
> R code, given other processes that are running and before any disk swapping
> kicks in. You might want to review R Windows FAQ 2.9, if you have not
> already:
>
>
> http://cran.r-project.org/bin/windows/base/rw-FAQ.html#There-seems-to-be-a-limit-on-the-memory-it-uses_0021
>
> For Windows on the MacBook, are you using Boot Camp to run Windows natively
> or are you using virtualization (eg. Parallels, VMWare, VirtualBox) to run
> Windows under OSX? If the latter, some of the time increase will be due to
> the virtualization overhead.
>
> You should be using the same version of R across each platform for a fair
> comparison, as there is also the potential, if not the likelihood, that some
> code has been improved between versions, which may yield some performance
> differences. 32 bit versus 64 bit will also yield some differences.
> Differences in tuned BLAS libraries across each OS can also account for
> performance differences. You should look into using the one provided by R
> across each to enable more balanaced comparisons.
>
> I am also not sure of what differences across each Windows test is
> attributable to WinXP versus Vista. There are others here with more insight
> into that aspect of things.
>
> While there is a consistent increase for Windows timing as you have above,
> some of the differences may be due to not really having a (pardon the pun)
> "Apples to Apples" comparison across each platform.
>
> HTH,
>
> Marc Schwartz
>
>

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