@inproceedings{8d4a8dd0420c4dd890af668159ef9330,
title = "Learning with analytical models",
abstract = "To understand and predict the performance of scientific applications, several analytical and machine learning approaches have been proposed, each having its advantages and disadvantages. In this paper, we propose and validate a hybrid approach for performance modeling and prediction, which combines analytical and machine learning models. The proposed hybrid model aims to minimize prediction cost while providing reasonable prediction accuracy. Our validation results show that the hybrid model is able to learn and correct the analytical models to better match the actual performance. Furthermore, the proposed hybrid model improves the prediction accuracy in comparison to pure machine learning techniques while using small training datasets, thus making it suitable for hardware and workload changes.",
keywords = "Analytical modeling, Hybrid modeling, Machine learning, Performance prediction",
author = "Huda Ibeid and Siping Meng and Oliver Dobon and Luke Olson and William Gropp",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 33rd IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2019 ; Conference date: 20-05-2019 Through 24-05-2019",
year = "2019",
month = may,
doi = "10.1109/IPDPSW.2019.00128",
language = "English (US)",
series = "Proceedings - 2019 IEEE 33rd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "778--786",
booktitle = "Proceedings - 2019 IEEE 33rd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2019",
address = "United States",
}