Re: [R] Parallel R

From: Martin Morgan <mtmorgan_at_fhcrc.org>
Date: Thu, 10 Jul 2008 09:19:54 -0700

"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).

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.
>>
>
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-- 
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

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Received on Thu 10 Jul 2008 - 16:25:37 GMT

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