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.
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