Hi,
I am doing a computational demanding time series analysis in R with a lot of for-loops which do the same analysis several times (e.g. for 164 patients, for 101 different time series per patient or for different time lags). In the end, the results of these analyses are summarized to one score per patient, but till this point, they work absolutely independent of each other. To shorten the computing time, I would like to parallelize the analysis. The independent parts could be analyzed parallel using not only one of the 8 cores of my processor.
I read some postings about performing functions like apply with more than one core, but I am not shure how to implement the approaches.
Does anybody know a simple and comprehensible way of translating a classical sequential for-loop into a procedure which uses different cores simultaneously to run a few of the analyses parallel?
Thank you very much for every comment!
Best,
Brian
Have a look at the the doParallel and foreach package. I think it is the most convenient way to get loops calculated on multi-cores
An intro can be found at
https://cran.r-project.org/web/packages/doParallel/vignettes/gettingstartedParallel.pdf
Cheers
You may also consider EPCC's work on SPRINT, a parallel R implementation currently employed for gene analysis: https://www.epcc.ed.ac.uk/projects-portfolio/sprint-parallel-statistics-genetic-research
Hi Brian, SPRINT (not SPRING, mea culpa) is currently only available for Unix/Linux/Mac. To get it working on Windows you can install, say, MinGW (http://www.mingw.org/) on Windows and then install and employ SPRINT from within there.
The easiest way to take advantage of multiprocessors is the multicore package which includes the function mclapply(). mclapply() is a multicore version of lapply(). So any process that can use lapply() can be easily converted to an mclapply() process. However, multicore does not work on Windows. I wrote a blog post about this last year which might be helpful. The package Revolution Analytics created, doSMP, is NOT a multi-threaded version of R. It's effectively a Windows version of multicore.
If your work is embarrassingly parallel, it's a good idea to get comfortable with the lapply() type of structuring. That will give you easy segue into mclapply() and even distributed computing using the same abstraction.
Things get much more difficult for operations that are not "embarrassingly parallel".
Anyone interested in may find some help on this page:
https://nceas.github.io/oss-lessons/parallel-computing-in-r/parallel-computing-in-r.html
Cheers
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