Re: [R] advice about R for windows speed
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. -- View this message in context: http://old.nabble.com/advice-about-R-for-windows-speed-tp26428194p26432206.html Sent from the R help mailing list archive at Nabble.com. __ 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.
[R] advice about R for windows speed
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/ [[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.
Re: [R] advice about R for windows speed
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 __ 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.
Re: [R] advice about R for windows speed
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
