Hello, I know of some various methods out there to utilize multiple processors but am not sure what the best solution would be. First some things to note: I'm running dependent simulations, so direct parallel coding is out (multicore, doSnow, etc). I'm on Windows, and don't know C. I don't plan on learning C or any of the *nix languages.
My main concern deals with Multiple analyses on large data sets. By large I mean that when I'm done running 2 simulations R is using ~3G of RAM, the remaining ~3G is chewed up when I try to create the Gelman-Rubin statistic to compare the two resulting samples, grinding the process to a halt. I'd like to have separate cores simultaneously run each analysis. That will save on time and I'll have to ponder the BGR calculation problem another way. Can R temporarily use HD space to write calculations to instead of RAM? The second concern boils down to whether or not there is a way to split up dependent simulations. For example at iteration (t) I feed a(t-2) into FUN1 to generate a(t), then feed a(t), b(t-1) and c(t-1) into FUN2 to simulate b(t) and c(t). I'd love to have one core run FUN1 and another run FUN2, and better yet, a third to run all the pre-and post- processing tidbits! So if anyone has any suggestions as to a direction I can look into, it would be appreciated. Robin Jeffries MS, DrPH Candidate Department of Biostatistics UCLA 530-633-STAT(7828) [[alternative HTML version deleted]] ______________________________________________ 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.