Performance Prediction of NUMA Placement: A Machine-Learning Approach
Fanourios Arapidis, Vasileios Karakostas, Νικέλα Παπαδοπούλου, Konstantinos Stefanos Nikas, Georgios Goumas, Nectarios Koziris · 2018
In this paper we present a machine-learning approach to predict the impact on performance of core and memory placement in non-uniform memory access (NUMA) systems. The impact on performance depends on the architecture and the application's characteristics. We focus our study on features that can be easily extracted with hardware performance counters that are found in commodity off-the-self systems. We run various single-threaded benchmarks from Spec2006 and Parsec under different placement scenarios, and we use this benchmarking data to train multiple regression models that could serve as performance predictors. Our experimental results show notable accuracy in predicting the impact on performance with relatively simple prediction models.