On Wed, Feb 4, 2009 at 4:02 PM, Warren Young <war...@etr-usa.com> wrote:
> Feng Li wrote: > >> >> I have two R instances running at the same time, >> > > On the same computer, or on different computers? The first trial is only on my single computer with Quad CPU and more than 2G mem. > > > Is the number of Rs likely to change, or will it always be just the two? I am planning to do three tasks at same time. That will be instance A, instance B and instance C. There tasks are more or less the same. But one always depends on others results. > > > Is this a simple one-off problem, or are you breaking the problem up into > pieces so you can throw lots of hardware at it? This is just for one project. But if this is available, later I will try more on this! > > > Is there a simpler way to pass the data in A to B? >> > > Perhaps the simplest option is to write the data structure to a file, using > any of the several R ways to do that. When instance 2 sees that a file is > available, it slurps its contents in and works on it. The hard part is > making the second instance wait until the whole file is written out by the > first. You wouldn't want it to read in half the file then hit the end > because the first process hasn't finished writing out the file. I don't see > any good mechanism in R to fix this. > > A more robust option is to use sockets. This is suitable even within a > single machine. See ?make.socket. This solves the "how do I know when I've > got the full data structure problem" because the second process can just > keep reading until it gets an error indicating that the remote peer closed > the connection. Once you have the data structure in string form, you can > eval() it to get an R object suitable for munching on. Figuring out how to > pass the data might be the hardest part. deparse() might be the easiest > way. > > If you're hoping to scale this up to lots of processes, look into Rmpi. > This provides a very clean way for an R program on one computer to start > slaves on other computers and then pass data to them in native R structures. > Setting up MPI itself is not trivial, however. It's best when you already > have a cluster of computers linked with MPI. > > ______________________________________________ > 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. > -- Feng Li Department of Statistics Stockholm University 106 91 Stockholm, Sweden [[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.
