From: Prof Brian Ripley <ripley_at_stats.ox.ac.uk>

Date: Sat 11 Jun 2005 - 17:14:07 EST

Date: Sat 11 Jun 2005 - 17:14:07 EST

Your tests are of problems where you really should be using an optimized BLAS. But because those pointers are twice the size, the L1 cache will hold half as many and so I am not surprised at a factor of three on a naive implementation.

For linear algebra on large matrices the key to good performance is to keep L1 cache misses to a minimum. Using SunPerf and a 1000x1000 problem I got

32-bit

[1] 4.99 0.03 5.02 0.00 0.00

64-bit

[1] 5.25 0.03 5.29 0.00 0.00

and for your regression problem

32-bit

[1] 24.97 0.96 26.15 0.00 0.00

64-bit

[1] 26.25 1.06 27.52 0.00 0.00

So the moral appears to be to take the advice in the R-admin manual and tune your linear algebra system.

On Fri, 10 Jun 2005, Scott Gilpin wrote:

> 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 /usr/local/lib/gcc/sparc-sun-solaris2.8/3.4.2/specs
**> Configured with: ../configure --with-as=/usr/ccs/bin/as
**> --with-ld=/usr/ccs/bin/ld --disable-nls
**> Thread model: posix
**> gcc version 3.4.2
**>
**> I built the 32 bit version of R with no changes to config.site. I
**> built the 64 bit version with the following in config.site:
**>
**> CC="gcc -m64"
**> FFLAGS="-m64 -g -02"
**> LDFLAGS="-L/usr/local/lib/sparcv9 -L/usr/local/lib"
**> CXXFLAGS="-m64 -g -02"
**>
**> neither build uses a BLAS. Both builds are installed on the same
**> machine, and the same disk. The machine has virtually no load; R is
**> one of the only processes running during these timings:
**>
**> First comparison: solve on a large matrix
**>
**>> echo 'set.seed(1);M<-matrix(rnorm(9e6),3e3);system.time(solve(M))' |
**> /disk/loneres01/R-2.1.0-32bit/bin/R -q --vanilla
**>> set.seed(1);M<-matrix(rnorm(9e6),3e3);system.time(solve(M))
**> [1] 713.45 0.38 713.93 0.00 0.00
**>>
**>
**>> echo 'set.seed(1);M<-matrix(rnorm(9e6),3e3);system.time(solve(M))' |
**> /disk/loneres01/R-2.1.0-64bit/bin/R -q --vanilla
**>> set.seed(1);M<-matrix(rnorm(9e6),3e3);system.time(solve(M))
**> [1] 2277.05 0.31 2278.38 0.00 0.00
**>>
**>
**> Second comparison: linear regression
**>
**> lonetree /home/sgilpin/R >echo 'set.seed(1);
**> y<-matrix(rnorm(10000*500),500);
**> x<-matrix(runif(500*100),500);
**> system.time(fit<-lm(y~x))' | /disk/loneres01/R-2.1.0-32bit/bin/R -q --vanilla
**>> set.seed(1);y<-matrix(rnorm(10000*500),500);x<-matrix(runif(500*100),500);system.time(fit<-lm(y~x))
**> [1] 23.34 0.80 24.17 0.00 0.00
**>>
**>
**> lonetree /home/sgilpin/R >echo 'set.seed(1);
**> y<-matrix(rnorm(10000*500),500);
**> x<-matrix(runif(500*100),500);
**> system.time(fit<-lm(y~x))' | /disk/loneres01/R-2.1.0-64bit/bin/R -q --vanilla
**>> set.seed(1);y<-matrix(rnorm(10000*500),500);x<-matrix(runif(500*100),500);system.time(fit<-lm(y~x))
**> [1] 55.34 0.70 56.21 0.00 0.00
**>>
**>
**> Final comparison: stats-Ex.R (from R-devel)
**> lonetree /home/sgilpin/R >time /disk/loneres01/R-2.1.0-32bit/bin/R -q
**> --vanilla CMD BATCH stats-Ex.R
**>
**> real 1m4.042s
**> user 0m47.400s
**> sys 0m10.390s
**> lonetree /home/sgilpin/R >time /disk/loneres01/R-2.1.0-64bit/bin/R -q
**> --vanilla CMD BATCH stats-Ex.R
**>
**> real 1m20.017s
**> user 1m3.590s
**> sys 0m10.130s
**>
**> I've seen Prof. Ripley and others comment that a 64 bit build will be
**> a little slower because the pointers are larger, and gc() will take
**> longer, but these timings seem out of this range.
**>
**> Any thoughts?
**>
**> ______________________________________________
**> R-help@stat.math.ethz.ch mailing list
**> https://stat.ethz.ch/mailman/listinfo/r-help
**> PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
**>
*

-- Brian D. Ripley, ripley@stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.htmlReceived on Sat Jun 11 17:22:10 2005

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