Nikos A: ... > > To have some results for my work, I also run the procedures on smaller > > subsets (say 3000 observations instead of 18865) which takes some time > > but is feasible @home.
Some timings: I've completed the procedures on smaller subsets (in total 10). The subsets (data.frames) contain 3000 random observations with 6 variables and 1 factor of 7 levels. - Each data.frame took about 41 minutes on quad-c...@3ghz/8GB-RAM. - Also, data.frames with 1040 observations, 6 variables, 1 factor with 5 levels took about 10 minutes each on [email protected]/6GB-RAM. > > Currently there is a process running on a big cluster (thanks to a very > > kind person who's always there). Hopefully we'll know soon enough how > > much time this will take. Markus N: > The job is still running on "my" blade :) Using 68GB of RAM. > Does anyone in the list have experience in running R on a multicore > system? This list is rather overwhelming for me: > > http://cran.r-project.org/web/views/HighPerformanceComputing.html > > An openMP approach or likewise with implicit parallelism would be great > since I cannot rewrite R... I would certainly try to work on this if pointed to the right direction (no previous experience on this though) although my time is running away... Thanks for all, Nikos _______________________________________________ grass-stats mailing list [email protected] http://lists.osgeo.org/mailman/listinfo/grass-stats
