[R-SIG-Mac] would parallel computing help?
Simon Urbanek
simon.urbanek at r-project.org
Tue Mar 8 15:15:59 CET 2011
On Mar 8, 2011, at 5:45 AM, Ben Bond-Lamberty wrote:
> Alan,
> The multicore package is easy to use and, if you problem is indeed
> embarrassingly parallel (there's no communication between different
> models? how about between chains?) should be straightforward to add.
> Note that you'll need to run any multicore-using script from the
> Terminal command line, and not from the Mac GUI, though.
FWIW since multicore 0.1-4 and R 2.12.2 it should be possible to run multicore in the Mac GUI (as long as you don't explicitly call GUI or graphics code in the parallel parts).
Cheers,
Simon
> Ben
>
> On Tue, Mar 8, 2011 at 3:14 AM, Alan Kelly <AKELLY at tcd.ie> wrote:
>> Dear all, I'm running a number of Bayesian binomial regression models using jags (interfacing with R via R2jags) on a Mac server with quad core processor running at 2.66 Ghz with 6 GB memory under Snow Leopard (session info below). As the models contain around 30 predictors and between 5 to 15 thousand observations, the time required to run a single model with 3 chains with an adequate number of iterations to ensure convergence is around 2 hours. While I can live with this for the occasional run, it will be a problem when I need to run several dozen different models.
>> Perhaps some of you have relevant experience and can advise if this run time could be significantly reduced using, for example, one of the parallel computing packages? And if so, which one? I should add that I'm not clear if jags can directly avail of multicore processing even if available - it might be necessary to program a Gibbs or Metropolis sampler directly in R.....
>> Any thoughts/suggestions?
>> Best wishes,
>> Alan Kelly
>>
>> sessionInfo()
>> R version 2.12.1 (2010-12-16)
>> Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)
>>
>> locale:
>> [1] en_IE.UTF-8/en_IE.UTF-8/C/C/en_IE.UTF-8/en_IE.UTF-8
>>
>> attached base packages:
>> [1] splines stats graphics grDevices utils datasets methods base
>>
>> other attached packages:
>> [1] car_2.0-9 survival_2.36-2 nnet_7.3-1 MASS_7.3-9 foreign_0.8-41
>>
>> loaded via a namespace (and not attached):
>> [1] tools_2.12.1
>>
>>
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