similar to: Performance difference between 32-bit build and 64-bit bu ild on Solaris 8

Hi everyone - I'm seeing a 32-bit build perform significantly faster (up to 3x) than a 64 bit build on Solaris 8. I'm running R version 2.1.0. Here are some of my system details, and some resulting timings: >uname -a SunOS lonetree 5.8 Generic_117350-16 sun4u sparc SUNW,Sun-Fire-V440 lonetree /home/sgilpin >gcc -v Reading specs from

Dear R-devel, Has anyone played with this? http://www.amd.com/us-en/Processors/DevelopWithAMD/0,,30_2252_2282,00.html <http://www.amd.com/us-en/Processors/DevelopWithAMD/0,,30_2252_2282,00.html> . I'll probably give it a shot... Best, Andy Andy Liaw, PhD Biometrics Research PO Box 2000, RY33-300 Merck Research Labs Rahway, NJ 07065 mailto:andy_liaw@merck.com

I have been timing a particular model fit using lmer on several different computers and came up with a peculiar result - the model fit is considerably slower on a dual-core Athlon 64 using Goto's multithreaded BLAS than on a single-core processor. Here is the timing on a single-core Athlon 64 3000+ running under today's R-devel with version 0.995-5 of the Matrix package. >

Greetings: On a Sun Solaris 8 machine using the Sun Studio 11 compiler: taz$ ./configure LDFLAGS=-xarch=v9 CFLAGS='-xO3 -xarch=v9' CC=/opt/SUNWspro/bin/cc --prefix=/opt/openssh-4.3p1 --with-ssl-dir=/opt/openssl-0.9.8a --with-xauth=/usr/openwin/bin/xauth --with-default-path=/bin:/sbin:/usr/sbin:/usr/bin:/usr/local/bin:/usr/openwin/bin:/usr/dt/bin --without-zlib-version-check checking

Dear all, Has anyone seen the same problem? I tried compiling R-1.9.0 alpha (2004-03-16) on our Opteron box running SUSE Linux ES8. I ran: ./configure --enable-R-shlib --with-blas=goto and got: Source directory: . Installation directory: /usr/local C compiler: gcc -m64 -O2 -g -msse2 -march=k8 -Wall --pedantic C++ compiler: g++ -m64 -O2 -g

For those interested: I compiled R-1.9.0beta_2004-04-02 with pgf77/gcc/g++ and g77/gcc/g++, both _without_ fast BLAS. Both passed make check-all. Simple crossprod(m) for fairly large `m' shows that the version compiled with pgf77 is about 15% faster: [andy@leo:scratch]% echo "set.seed(1); m <- matrix(runif(5e7), 1e4); gc(); system.time(crossprod(m))" | R-1.9.0-PGI/bin/R

I have a problem configuring my install of samba on solaris, version 3.0.22 which was downloaded from the link below. It appears to install correctly when running the 'pkgadd -d <file>' command, but i wish to be able to configure it to use the winbindd daemon (i.e. run ./configure --with-winbind) but i am unsure how i do this as i am unable to find the configure utility to be able

Hi, I'm working on a server with a sparcv9 chip using SunOS 5.9 Generic May 2002. The compilers are the Sun Studio 12 compilers. I'm trying to build a 64-bit version of R-2.6.1, and while the configure script runs, the make does not. Here are the options I set in config.site: ________________________________________________________________________ _ R_PAPERSIZE=letter CC="cc

> From: Douglas Bates > > How does the Opteron perform on floating point? Can you try something > like > > > mm = matrix(rnorm(1e6), nc = 1e3) > > system.time(crossprod(mm)) > [1] 0.51 0.02 0.53 0.00 0.00 > > system.time(crossprod(mm)) > [1] 0.37 0.03 0.40 0.00 0.00 > > system.time(crossprod(mm)) > [1] 0.38 0.02 0.40 0.00 0.00 > >

The following is at least as much out of intellectual curiosity as for practical reasons. On reviewing some code written by novices to R, I came across: crossprod(x, y)[1,1] I thought, "That isn't a very S way of saying that, I wonder what the penalty is for using 'crossprod'." To my surprise the penalty was substantially negative. Handily the client had S-PLUS as

Dear R community, The next release of package ff is available on CRAN. With kind help of Brian Ripley it now supports the Win64 and Sun versions of R. It has three major functional enhancements: a) new fast in-memory sorting and ordering functions (single-threaded) b) ff now supports on-disk sorting and ordering of ff vectors and ffdf dataframes c) ff integer vectors now can be used as

Dear R community, The next release of package ff is available on CRAN. With kind help of Brian Ripley it now supports the Win64 and Sun versions of R. It has three major functional enhancements: a) new fast in-memory sorting and ordering functions (single-threaded) b) ff now supports on-disk sorting and ordering of ff vectors and ffdf dataframes c) ff integer vectors now can be used as

https://bugzilla.samba.org/show_bug.cgi?id=8090 Summary: full_fname out of memory error on missing file SunOS 5.8 Product: rsync Version: 3.0.7 Platform: Sparc OS/Version: SunOS Status: NEW Severity: minor Priority: P5 Component: core AssignedTo: wayned at samba.org

Dear R-Users, I am trying to use paste to paste the character ' around a character vector. Paste appears to place spaces (" ") around this. Anyway this can be done without the unintended introduction of space characters ? Please see example below. Many thanks, Tolga > charlist<-c("a","b","c") > lapply(charlist, function (y)

Dear R-devel: The Cluster administrators at KU got enthusiastic about testing R-3.2.2 with Intel MKL when I asked for some BLAS integration. Below I forward a performance report, which is encouraging, and thought you would like to know the numbers. Appears to my untrained eye there are some extraordinary speedups on Cholesky decomposition, determinants, and matrix inversion. They had

Dear all, One of my students (whom I am trying to convince to use R) wants to get a fairly large SAS dataset into R (about 150mB). An obvious and simple thing she tried was to write the dataset as a .csv-file and then read that into R, but that takes forever (or something close to that..). The dataset is so large, that exporting it as an Excel file from SAS is not feasible (more than 65000 lines).

Hi all, Apologies if this has been raised before ... R's tapply is very fast, but if X has names in this example, there seems to be a huge slow down: under 1 second compared to 151 seconds. The following timings are repeatable and are timed properly on a single user machine : > X = 1:100000 > names(X) = X > system.time(fast<<-tapply(as.vector(X), rep(1:10000,each=10), mean))

On Thu, Apr 17, 2014 at 09:57:32AM -0400, Tom Stellard wrote: > On Fri, Apr 11, 2014 at 03:02:21PM -0700, Tom Stellard wrote: > > Hi, > > > > I have just tagged the first release candidate for the > > 3.4.1 release, so testers may begin testing. Please refer to > > http://llvm.org/docs/ReleaseProcess.html for information on how to > > validate a release.

I was surprised to find that df$a[1] is an order of magnitude faster than df[1,"a"]: > df <- data.frame(a=1:10) > system.time(replicate(100000, df$a[3])) user system elapsed 0.36 0.00 0.36 > system.time(replicate(100000, df[3,"a"])) user system elapsed 4.09 0.00 4.09 A priori, I'd have thought that combining the row and column

Hi Paul, We've been through this process ourselves for the Revolution R Open project. There are a number of pitfalls to avoid, but you can take a look at how we achieved it in the build scripts at: https://github.com/RevolutionAnalytics/RRO There are also some very useful notes in the R Installation guide: https://cran.r-project.org/doc/manuals/r-release/R-admin.html#BLAS Most packages do