Mantis: Predicting System Performance through Program Analysis and Modeling
Abstract
We present Mantis, a new framework that automatically predicts program performance with high accuracy. Mantis integrates techniques from programming language and machine learning for performance modeling, and is a radical departure from traditional approaches. Mantis extracts program features, which are information about program execution runs, through program instrumentation. It uses machine learning techniques to select features relevant to performance and creates prediction models as a function of the selected features. Through program analysis, it then generates compact code slices that compute these feature values for prediction. Our evaluation shows that Mantis can achieve more than 93% accuracy with less than 10% training data set, which is a significant improvement over models that are oblivious to program features. The system generates code slices that are cheap to compute feature values.
Keywords
Cite
@article{arxiv.1010.0019,
title = {Mantis: Predicting System Performance through Program Analysis and Modeling},
author = {Byung-Gon Chun and Ling Huang and Sangmin Lee and Petros Maniatis and Mayur Naik},
journal= {arXiv preprint arXiv:1010.0019},
year = {2010}
}