Using machine learning to optimize graph execution on NUMA machines

Abstract

This paper proposes PredG, a Machine Learning framework to enhance the graph processing performance by finding the ideal thread and data mapping on NUMA systems. PredG is agnostic to the input graph: it uses the available graphs' features to train an ANN to perform predictions as new graphs arrive - without any application execution after being trained. When evaluating PredG over representative graphs and algorithms on three NUMA systems, its solutions are up to 41% faster than the Linux OS Default and the Best Static - on average 2% far from the Oracle -, and it presents lower energy consumption.