The Art of R Programming

Resorting to C

As you’ve seen, using parallel R may greatly speed up your R code. This allows you to retain the convenience and expressive power of R, while still ameliorating large runtimes in big applications. If the parallelized R gives you sufficiently good performance, then all is well.

Nevertheless, parallel R is still R and thus still subject to the performance issues covered in Chapter 14. Recall that one solution offered in that chapter was to write a performance-critical portion of your code in C and then call that code from your main R program. (The references to C here mean C or C++.) We will explore this from a parallel-processing viewpoint. Here, instead of writing parallel R, we write ordinary R code that calls parallel C. (I assume ...