Parallelism In R

Kevin Feasel

2017-09-06

R

Florian Prive shows off a few methods for parallelizing code in R:

Parallelize with foreach

You need to do at least two things:

  • replace %do% by %dopar%. Basically, always use %dopar% because you can use registerDoSEQ() is you really want to run the foreach sequentially.

  • register a parallel backend using one of the packages that begin with do (such as doParalleldoMCdoMPI and more). I will list only the two main parallel backends because there are too many of them.

Check it out.  Florian spends a lot of time with foreach and doParallel, a little bit of time with flock, and mentions Microsoft R Open.  H/T R-Bloggers

Related Posts

Biases in Tree-Based Models

Nina Zumel looks at tree-based ensembling models like random forest and gradient boost and shows that they can be biased: In our previous article , we showed that generalized linear models are unbiased, or calibrated: they preserve the conditional expectations and rollups of the training data. A calibrated model is important in many applications, particularly when financial data […]

Read More

R 3.6.1 Available

David Smith notes a new version of R is available: On July 5, the R Core Group released the source code for the latest update to R, R 3.6.1, and binaries are now available to download for Windows, Linux and Mac from your local CRAN mirror. R 3.6.1 is a minor update to R that fixes a few bugs. […]

Read More

Categories

September 2017
MTWTFSS
« Aug Oct »
 123
45678910
11121314151617
18192021222324
252627282930