I was under the impression that R has been run on 64-bit Solaris (and other 64-bit Unices) for quite a while (as 64-bit app). We've been running 64-bit R on amd64 for a few months (and had quite a few oppertunities to get the R processes using over 8GB of RAM). Not much problem as far as I can see...
Best, Andy > From: Roger D. Peng > > As far as I know, R does compile on AMD Opterons and runs as a > 64-bit application. So it can store objects larger than 4GB. > However, I don't think R gets tested very often on 64-bit > machines with such large objects so there may be yet undiscovered > bugs. > > -roger > > Sunny Ho wrote: > > > Hello everyone, > > > > I would like to get some advices on using R with some > really large datasets. > > > > I'm using RH9 Linux R 1.8.1 for a research with a lot of > numerical data. The datasets total to around 200Mb (shown by > memory.size). During my data manipulation, the system memory > usage grew to 1.5Gb, and this caused a lot of swapping > activities on my 1Gb PC. This is just a small-scale > experiment, the full-scale one will be using data 30 times as > large (on a 4Gb machine). I can see that I'll need to deal > with memory usage problem very soon. > > > > I notice that R keeps all datasets in memory at all times. > I wonder whether there is any way to instruct R to push some > of the less-frequently-used data tables out of main memory, > so as to free up memory for those that are actively in used. > It'll be even better if R can keep only part of a table in > memory only when that part is needed. Using save & load could > help, but I just wonder whether R is intelligent enough to do > this by itself, so I don't need to keep track of memory usage > at all times. > > > > Another thought is to use a 64-bit machine (AMD64). I find > there is a pre-compiled R for Fedora Linux on AMD64. Anyone > knows whether this version of R runs as 64-bit? If so, then > will R be able to go beyond the 32-bit 4Gb memory limit? > > > > Also, from the manual, I find that the RPgSQL package (for > PostgreSQL database) supports a feature "proxy data frame". > Does anyone have experience with this? Can "proxy data frame" > handle memory efficiently for very large datasets? Say, if I > have a 6Gb database table defined as a proxy data frame, will > R & RPgSQL be able to handle it with just 4Gb of memory? > > > > Any comments will be useful. Many thanks. > > > > Sunny Ho > > (Hong Kong University of Science & Technology) > > > > ______________________________________________ > > [EMAIL PROTECTED] mailing list > > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html > > > > > ______________________________________________ > [EMAIL PROTECTED] mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html > > ------------------------------------------------------------------------------ Notice: This e-mail message, together with any attachments,...{{dropped}} ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
