"Juan Pablo Romero Méndez" <[EMAIL PROTECTED]> writes: > Just out of curiosity, what system do you have? > > These are the results in my machine: > >> system.time(exp(m), gcFirst=TRUE) > user system elapsed > 0.52 0.04 0.56 >> library(pnmath) >> system.time(exp(m), gcFirst=TRUE) > user system elapsed > 0.660 0.016 0.175 >
from cat /proc/cpuinfo, the original results were from a 32 bit dual-core system model name : Intel(R) Core(TM)2 CPU T7600 @ 2.33GHz Here's a second set of results on a 64-bit system with 16 core (4 core on 4 physical processors, I think) > mean(replicate(10, system.time(exp(m), gcFirst=TRUE))["elapsed",]) [1] 0.165 > mean(replicate(10, system.time(exp(m), gcFirst=TRUE))["elapsed",]) [1] 0.0397 model name : Intel(R) Xeon(R) CPU X7350 @ 2.93GHz One thing is that for me in single-thread mode the faster processor actually evaluates slower. This could be because of 64-bit issues, other hardware design aspects, the way I've compiled R on the two platforms, or other system activities on the larger machine. A second thing is that it appears that the larger machine only accelerates 4-fold, rather than a naive 16-fold; I think this is from decisions in the pnmath code about the number of processors to use, although I'm not sure. A final thing is that running intensive tests on my laptop generates enough extra heat to increase the fan speed and laptop temperature. I sort of wonder whether consumer laptops / desktops are engineered for sustained use of their multiple core (although I guess the gaming community makes heavy use of multiple cores). Martin > Juan Pablo > > >> >>> system.time(exp(m), gcFirst=TRUE) >> user system elapsed >> 0.108 0.000 0.106 >>> library(pnmath) >>> system.time(exp(m), gcFirst=TRUE) >> user system elapsed >> 0.096 0.004 0.052 >> >> (elapsed time about 2x faster). Both BLAS and pnmath make much better >> use of resources, since they do not require multiple R instances. >> > > ______________________________________________ > 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. -- Martin Morgan Computational Biology / Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 Location: Arnold Building M2 B169 Phone: (206) 667-2793 ______________________________________________ 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.
