Performance benefits of linking R to multithreaded math libraries

June 11, 2010
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R wasn’t originally designed as a multithreaded application — multiprocessor systems were still rare when the R Project was first conceived in the mid-90’s — and so, by default, R will only use one processor of your dual-core laptop or quad-core desktop machine when doing calculations. For calculations that take a long time, like big simulations or modeling of large data sets, it would be nice to put those other processors to use to speed up the computations. There are several parallel processing libraries for R available that allow you to explicitly run loops in R simultaneously (ideally, each on a different processor), but using them does require you to rewrite your code accordingly. 

But there is a way to make use of all your processing power for many computations in R, without changing a line of code. That’s because R is a statistical computing system, and at the heart of many of the algorithms you use on a daily basis — data restructuring, regressions, classifications, even some graphics functions — is linear algebra. The data are transformed into vector and matrix objects, and the internals of R have been cleverly designed to link to a standard “BLAS” API to
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