  Bayesian Additive Regression Trees(BART) is a Bayesian nonparametric approach
which has been shown to be competitive with the best modern predictive methods
such as random forest and Gradient Boosting Decision Tree.The sum of trees
structure combined with a Bayesian inferential framework provide a accurate and
robust statistic method.BART variant named SBART using randomized decision
trees has been developed and show practical benefits compared to BART. The
primary bottleneck of SBART is the speed to compute the sufficient statistics
and the publicly avaiable implementation of the SBART algorithm in the R
package is very slow.In this paper we show how the SBART algorithm can be
modified and computed using single program,multiple data(SPMD) distributed
computation with the Message Passing Interface(MPI) library.This approach
scales nearly linearly in the number of processor cores, enabling the
practitioner to perform statistical inference on massive datasets. Our approach
can also handle datasets too massive to fit on any single data repository.We
have made modification to this algorithm to make it capable to handle
classfication problem which can not be done with the original R package.With
data experiments we show the advantage of distributed SBART for classfication
problem compared to BART.
