Cross prediction is an active research area. Many research works have used cross prediction to predict the target system's performance and power from the machine learning model trained on the source system. The source and target systems differ either in terms of instruction-set or hardware features. A widely used transfer learning technique utilizes the knowledge from a trained machine learning from one problem to predict targets in similar problems. In this work, we use transfer learning to achieve cross-system and cross-platform predictions. In cross-system prediction, we predict the physical system's performance (runtime) and power from the simulation systems dataset while predicting performance and the power for target system from source system both having different instruction-set in cross-platform prediction. We achieve runtime prediction accuracy of 90% and 80% and power prediction accuracy of 75% and 80% in cross-system and cross-platform predictions, respectively, for the best performing deep neural network model. Furthermore , we have evaluated the accuracy of univariate and multivariate machine learning models, the accuracy of compute-intensive and data-intensive applications, and the accuracy of the simulation and physical systems.
Our results for performance prediction show that the tree-based machine-learning models outperform all other models with median absolute percentage error 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 cross-systems 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