[R] Parallel R

Martin Morgan mtmorgan at fhcrc.org
Thu Jul 10 18:19:54 CEST 2008

"Juan Pablo Romero Méndez" <jpablo.romero at gmail.com> 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).


>   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 at 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

More information about the R-help mailing list