Dear prof, and list! I'm wondering which are the steps to exploit multiple processors/cores if most of the processing time is due to C code dynamically loaded into R. I mean; e.g., a Monte Carlo analysis calls the C part a huge number of times, and it is this C part which takes most of the time.
Will snow be anyway useful for this, or multithreading must be made explicit (I don't know how) within the C code, or there is nothing we can do? Javier G.P ---- > On Tue, 7 Oct 2008, pejpm wrote: > >> >> I will preface this message by saying that I am not an R developer and >> no >> very little about R...but here is my situation: >> >> One of my users has developed a model for analysing commodity prices. At >> the >> moment when he runs this model on his daily data set it takes roughly 5 >> hours to complete. He is using a quad core PC with 2gb of RAM. The R >> process >> only uses 1 core..i.e. the overall CPU usage tops out at around 25%. >> This >> has been a managable situation for a while, but he would now like to run >> this model on 5 years of historical data. He has a colleague who ran the >> model on a 16 core Redhat Linux box, but it took even longer to run. He >> has >> asked me for assistance in speeding up this process. I have a couple of >> questions: >> >> 1) Is is possible to run the Windows version of R across all four >> processors? > > No. > >> 2) I was under the impression that R for Linux supported multi-threading >> by >> default. Am I correct in this assumption? If not, is it possible for >> Linux R >> to multi thread, and how do I go about configuring this? > > Your impression/assumption is wrong. > >> Apologies for the lack of detailed info in this post. I work in trade >> floor >> support and engineering and we dont really have much demand for this >> kind of >> heavy duty computational work so I am learning as I investigate this >> issue. > > R runs as a single task. It is possible that some of the the support > functions (notably the BLAS) can be multithreaded, and this will often > (but not always) help if the task is intensive numerical linear algebra. > But even if a multithreaded BLAS is used (and it is not the default > build), the effect on a typical R task is very small. > > If you want to exploit multiple processors/cores you need to split up your > R job amongst multiple processes. There are ways to help you do that > (packages snow and Rmpi, amongst others), but they need recoding of the > job to make use of them. > > -- > Brian D. Ripley, [EMAIL PROTECTED] > Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ > University of Oxford, Tel: +44 1865 272861 (self) > 1 South Parks Road, +44 1865 272866 (PA) > Oxford OX1 3TG, UK Fax: +44 1865 272595 > > ______________________________________________ > 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. > ______________________________________________ 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.
