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_schwa...@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 > > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
