In general, R is not written in such a way that data remain in cache. However, R can use optimized BLAS libraries, and these are. So if your version of R is compiled to use an optimized BLAS library appropriate to the machine (e.g., ATLAS, or Prof. Goto's Blas), AND a considerable amount of the computation done in your R program involves basic linear algebra (matrix multiplication, etc.), then you might see a good speedup.
-- Tony Plate Kimpel, Mark William wrote: > I am using R with Bioconductor to perform analyses on large datasets > using bootstrap methods. In an attempt to speed up my work, I have > inquired about using our local supercomputer and asked the administrator > if he thought R would run faster on our parallel network. I received the > following reply: > > > > > > "The second benefit is that the processors have large caches. > > Briefly, everything is loaded into cache before going into the > processor. With large caches, there is less movement of data between > memory and cache, and this can save quite a bit of time. Indeed, when > programmers optimize code they usually think about how to do things to > keep data in cache as long as possible. > > Whether you would receive any benefit from larger cache depends on how > R is written. If it's written such that data remain in cache, the > speed-up could be considerable, but I have no way to predict it." > > > > My question is, "is R written such that data remain in cache?" > > > > Thanks, > > > > > > Mark W. Kimpel MD > > > > Indiana University School of Medicine > > > > > [[alternative HTML version deleted]] > > ______________________________________________ > [email protected] mailing list > https://stat.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://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
