Parallel Programming with GPUs and R by Norman Matloff.
From the post:
You’ve heard that graphics processing units — GPUs — can bring big increases in computational speed. While GPUs cannot speed up work in every application, the fact is that in many cases it can indeed provide very rapid computation. In this tutorial, we’ll see how this is done, both in passive ways (you write only R), and in more direct ways, where you write C/C++ code and interface it to R.
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Norman provides as readable an introduction to GPUs as I have seen in a while, a quick overview of R packages for accessing GPUs and then a quick look at writing CUDA code and problems you may have compiling it.
Of particular note is Norman’s reference to a draft of his new book, Parallel Computation for Data Science, which introduces parallel computing with R.
It won’t be long before parallel or not processing will be a detail hidden from the average programmer. Until then, see this post and Norman’s book for parallel processing and R.