Machine Learning Algorithm Performance on the Lucata Computer

Abstract

A new parallel computing paradigm has recently become available, one that combines a PIM (processor in memory) architecture with the use of many lightweight threads, where each thread migrates automatically to the memory used by that thread. Our effort focuses on producing performance gains on this architecture for a key machine learning algorithm, Random Forest, that are at least linear in proportion to the number of cores. Beyond that, we show that a data distribution that groups test samples and trees by feature improves run times by a factor more than double the number of cores in the machine.