Performance and power modeling on disparate computer systems using machine learning
Performance and Power prediction is an active area of research due to its applications in the advancements of hardware-software co-development. We have performed experiments to evaluate the performance of several machine learning models. Our results for performance prediction show that the tree-based machine-learning models outperform all other models with median absolute percentage error (MedAPE) of less than 5% followed by bagging and boosting models that help to improve weak learners. We have collected performance data both from simulation-based hardware as well as from physical systems and observed that prediction accuracy is higher on simulation-based hardware due to its deterministic nature as compared to physical systems. Moreover, in physical systems, prediction accuracy of memory-bound applications is higher as compared to compute-bound algorithms due to manufacturer variability in processors. Furthermore, our result shows the prediction error of 15% in case of crosssystems prediction whereas in case of the cross-platform prediction error of 17% for simulation-based X86 to ARM prediction and 23% for physical Intel Core to Intel-Xeon system using best performing tree-based machine-learning model. We have employed several machine learning univariate or multivariate models for our experiments. Our result shows that runtime and power prediction accuracy of more than 80% and 90% respectively is achieved for multivariate deep neural network model in cross-platform prediction. Similarly, for cross-system prediction runtime accuracy of 90% and power accuracy of 75% is achieved for the multivariate deep neural network.
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