[Rd] Peculiar timing result

Liaw, Andy andy_liaw at merck.com
Fri Mar 3 19:18:02 CET 2006 

Paul,

I think what you're seeing is the performance gap between 64-bit binary and
32-bit binary on x86_64.  I believe Prof. Ripley had mentioned this several
times in the past.

I do remember back when I was playing with optimized BLAS with R on 32-bit
Linux that I've seen something similar to what Prof. Bates is seeing, that
the one that uses threaded BLAS took longer the than non-threaded one (on
dual Xeon boxes).  I never dug into it more, though.

Andy

From: Paul Gilbert
> 
> Doug
> 
> This is probably not your reason, but I am finding my dual 
> core Athlon 
> 64 is much slower running 64 bit Linux and R than it was 
> running 32 bit 
> Linux and R. All the programs are bigger.  (Some, like the 
> clock applet, 
> are a lot bigger for no obvious reason.)  The difference is enough to 
> put my meager 1GB machine into swapping much more, with the 
> result that 
> it is a lot slower.
> 
> Paul
> 
> Douglas Bates wrote:
> 
> >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.
> >
> >  
> >
> >>library(Matrix)
> >>data(star, package = 'mlmRev')
> >>system.time(fm1 <- 
> >>lmer(math~gr+sx+eth+cltype+(yrs|id)+(1|tch)+(yrs|sch), 
> star, control = list(nit=0,grad=0,msV=1)))
> >>    
> >>
> >  0      241720.:  1.16440 0.335239  0.00000  1.78732 
> 0.867209 0.382318  0.00000
> >  1      239722.:  1.94952 5.00000e-10 0.00933767  1.65999 0.858003
> >0.341520 0.00908757
> >  2      239580.:  1.95924 0.0884059 0.00933767  1.65308 0.857487
> >0.339296 0.00954718
> >  3      239215.:  2.60877 0.0765848 0.0177699  1.45739 0.843562
> >0.275100 0.0236849
> >  4      239204.:  2.62582 0.106670 0.0239698  1.41976 0.841086
> >0.261033 0.0267073
> >  5      239176.:  2.63149 0.0787924 0.0367185  1.37952 0.838527
> >0.245076 0.0301134
> >  6      239141.:  2.64949 0.108534 0.0594586  1.28846 0.832543
> >0.208404 0.0375456
> >  7      239049.:  2.64794 0.0789214 0.121782  1.10436 0.819711
> >0.126101 0.0524965
> >  8      239004.:  2.66044 0.117957 0.181505 0.932202 0.798982
> >0.0718116 0.0628958
> >  9      238944.:  2.66310 0.0819653 0.334477 0.631735 0.740855
> >0.258359 0.0806590
> > 10      238893.:  2.72626 0.0975205 0.653432 0.703912 0.666067
> >0.109922 0.201809
> > 11      238892.:  2.74381 0.111146 0.666155 0.693544 
> 0.662000 0.104060 0.207591
> > 12      238888.:  2.75052 0.0990238 0.689199 0.694588 0.655781
> >0.106516 0.216460
> > 13      238861.:  2.80325 0.126935  1.05912 0.733914 
> 0.556162 0.159296 0.360938
> > 14      238832.:  2.82656 0.117617  1.59471 0.607916 0.441371
> >0.0749944 0.976142
> > 15      238811.:  2.87350 0.136332  1.59046 0.653141 
> 0.353763 0.226061  1.79285
> > 16      238810.:  2.87663 0.125135  1.58992 0.656808 
> 0.352605 0.220488  1.79282
> > 17      238806.:  2.89342 0.141551  1.58607 0.676523 
> 0.344212 0.181833  1.79268
> > 18      238804.:  2.90080 0.125137  1.56624 0.682921 
> 0.261295 0.180598  1.74325
> > 19      238802.:  2.91950 0.128569  1.56836 0.680436 
> 0.336051 0.159940  1.80400
> > 20      238801.:  2.92795 0.134762  1.56597 0.685121 
> 0.331695 0.145547  1.80414
> > 21      238801.:  2.93741 0.125667  1.56139 0.687827 
> 0.332700 0.138854  1.81495
> > 22      238800.:  2.94588 0.131757  1.55294 0.687909 
> 0.330414 0.137834  1.82826
> > 23      238799.:  2.96867 0.129716  1.52943 0.688678 
> 0.323171 0.139912  1.84615
> > 24      238799.:  2.98994 0.133378  1.52188 0.700038 
> 0.337387 0.131403  1.77623
> > 25      238799.:  3.00312 0.135308  1.51475 0.697550 
> 0.311750 0.145683  1.78037
> > 26      238799.:  3.00461 0.129920  1.51083 0.697666 
> 0.306722 0.138745  1.81188
> > 27      238799.:  3.00504 0.134882  1.50539 0.696745 
> 0.302949 0.135897  1.84405
> > 28      238799.:  3.00422 0.134013  1.47947 0.698115 
> 0.303243 0.133806  1.86486
> > 29      238799.:  3.00819 0.134378  1.48185 0.701871 
> 0.307097 0.134637  1.84996
> > 30      238799.:  3.01313 0.134279  1.49098 0.702883 
> 0.304788 0.133682  1.86254
> > 31      238799.:  3.01291 0.134253  1.49060 0.701818 
> 0.303155 0.133771  1.84613
> > 32      238799.:  3.01142 0.134314  1.48921 0.701782 
> 0.303589 0.134439  1.84653
> > 33      238799.:  3.01174 0.134315  1.48926 0.701641 
> 0.304120 0.134145  1.84635
> > 34      238799.:  3.01175 0.134304  1.48942 0.701740 
> 0.303762 0.134185  1.84649
> > 35      238799.:  3.01173 0.134307  1.48937 0.701724 
> 0.303809 0.134206  1.84647
> >[1] 43.10  3.78 48.41  0.00  0.00
> >
> >
> >(If you run the timing yourself and don't want to see the iteration 
> >output, take the msV=1 out of the control list.  I keep it 
> in there so 
> >I can monitor the progress.)
> >
> >If I time the same model fit on a dual-core Athlon 64 X2 
> 3800+ with the 
> >same version of R, BLAS and Matrix package, the timing ends up with 
> >something like
> >
> >90 140 235 0 0
> >
> >I do see that the multithreading is working on a calculation that is 
> >essentially BLAS-bound such as
> >
> >  
> >
> >>mm <- Matrix(rnorm(1e6), nc = 1e3)
> >>system.time(crossprod(mm))
> >>    
> >>
> >[1] 0.57 0.02 0.60 0.00 0.00
> >
> >On the X2 processor it still takes about 0.6 seconds user 
> time but only 
> >0.3 seconds elapsed time when the machine is otherwise idle and both 
> >cores are available for the calculation.
> >
> >Any suggestions why the dual-core processor is so much 
> slower than the 
> >single core processor?
> >
> >By the way, I would be interested in accumulating timings of 
> this model 
> >fit on other systems.  If you do time it please send me
> >(off-list) a summary of the version of R, version of the accelerated 
> >BLAS if you use them, processor speed and configuration (i.e. 
> >multiprocessor, multicore, etc.) and, if you know it, memory speed.
> >
> >This is an example of a complex multilevel model with 
> crossed grouping 
> >factors fit to a relatively large (30000 observations on 10000 
> >students, 1400 teachers and 80 schools) data set.
> >
> >______________________________________________
> >R-devel at r-project.org mailing list 
> >https://stat.ethz.ch/mailman/listinfo/r-devel
> >  
> >
> 
> ______________________________________________
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> https://stat.ethz.ch/mailman/listinfo/r-devel
> 
>

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