The Random Forest algorithm serves as a sort of Swiss Army Knife for data modeling. It is among a handful of commonly-applied predictive techniques, and is well known both for its robustness and its predictive power. Performance is a common drawback of the method, however, owing to a slow training phase. The Arborist is an open-source implementation of the algorithm intended to overcome key performance bottlenecks.

The Arborist's design goals include minimization of costly data movement, parallelization across a wide range of commodity hardware, language-agnostic implementation and ready internalization of common workflows. An R-language front-end has been available for several months, and a Python version is being made available for the general machine-learning community. Internalized workflows include quantile regression, as well as nonparametric resampling.

Popular approaches to accelerating the algorithm have included distribution of training across computational nodes using such tools as MPI or Hadoop. The Arborist augments these approaches by parallelizing training within individual trees as well across blocks, and by employing either - or both - multicore and GPGPU hardware, effecting a hierarchical parallel structure. The ability to do this is imparted by an innovative recasting of the algorithm focused on identifying, and conserving, data locality.

We outline the algorithmic structure of the Arborist and discuss some of the challenges posed in accelerating this popular technique.