Rth: a Flexible Parallel Computation Package for R by Norm Matloff.
From the post:
The key feature of Rth is in the word flexible in the title of this post, which refers to the fact that Rth can be used on two different kinds of platforms for parallel computation: multicore systems and Graphics Processing Units (GPUs). You all know about the former–it’s hard to buy a PC these days that is not at least dual-core–and many of you know about the latter. If your PC or laptop has a somewhat high-end graphics card, this enables extremely fast computation on certain kinds of problems. So, whether have, say, a quad-core PC or a good NVIDIA graphics card, you can run Rth for fast computation, again for certain types of applications. And both multicore and GPUs are available in the Amazon EC2 cloud service.
Rth Quick Start
Our Rth home page tells you the GitHub site at which you can obtain the package, and how to install it. (We plan to place it on CRAN later on.) Usage is simple, as in this example:
…
Rth is an example of what I call Pretty Good Parallelism (an allusion to Pretty Good Privacy). For certain applications it can get you good speedup on two different kinds of common platforms (multicore, GPU). Like most parallel computation systems, it works best on very regular, “embarrassingly parallel” problems. For very irregular, complex apps, one may need to resort to very detailed C code to get a good speedup.
Rth has not been tested on Windows so I am sure the authors would appreciate reports on your use of Rth with Windows.
Contributions of new Rth functions are solicited. At least if you don’t mind making parallel processing easier for everyone. 😉
I first saw this in a tweet by Christopher Lalanne.