By John Mount
As we demonstrated in “A gentle introduction to parallel computing in R” one of the great things about R is how easy it is to take advantage of parallel processing capabilities to speed up calculation. In this note we will show how to move from running jobs multiple CPUs/cores to running jobs multiple machines (for even larger scaling and greater speedup). Using the technique on Amazon EC2 even turns your credit card into a supercomputer.
Colossus supercomputer : The Forbin Project
R itself is not a language designed for parallel computing. It doesn’t have a lot of great user exposed parallel constructs. What saves us is the data science tasks we tend to use R for are themselves are very well suited for parallel programming and many people have prepared very good pragmatic libraries to exploit this. There are three main ways for a user to benefit from library supplied parallelism:
- Link against superior and parallel libraries such as the Intel BLAS library (supplied on Linux, OSX, and Windows as part of the Microsoft R Open distribution of R). This replaces libraries you are already using with parallel ones, and you get a speed up for free (on appropriate tasks, such as linear algebra portions of lm()/glm()).
- Ship your modeling tasks out of R into an external parallel system for processing. This is strategy of systems such as rx methods from RevoScaleR, now Microsoft Open R, h2o methods from h2o.ai, or RHadoop.
- Use R’s
parallelfacility to ship jobs to cooperating R instances. This is the strategy used in “A gentle introduction to parallel computing in R” and many libraries that sit on top ofparallel. This is essentially implementing remote procedure call through sockets or networking.
We are going to write more about the third technique.
The …read more
Source:: r-bloggers.com