would parallel computing help?
It would be easier to run multiple copies of R. Ken
On 08/03/2011, at 7:14 PM, Alan Kelly 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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