[R] advice about R for windows speed

Fri Nov 20 09:52:30 CET 2009

I can only speak for the Windows part, but isn't it possible to test on
Windows 7 64bit?
(you can download a test version, and use a dual boot setup to try)
I always heard about the difficulties with WinXP 64bit, so maybe that is
just the problem?
Another thing to find out: maybe you can take advantage of the 4 processors
of your quadcore by using snow or similar?

Bart

Carlos Hernandez-7 wrote:
> 
> 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 at 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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