Learning with analytical models

Huda Ibeid, Siping Meng, Oliver Dobon, Luke Olson, William Gropp

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2019 IEEE 33rd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages778-786
Number of pages9
ISBN (Electronic)9781728135106
DOIs
StatePublished - May 2019
Event33rd IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2019 - Rio de Janeiro, Brazil
Duration: May 20 2019May 24 2019

Publication series

NameProceedings - 2019 IEEE 33rd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2019

Conference

Conference33rd IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2019
CountryBrazil
CityRio de Janeiro
Period5/20/195/24/19

    Fingerprint

Keywords

  • Analytical modeling
  • Hybrid modeling
  • Machine learning
  • Performance prediction

ASJC Scopus subject areas

  • Information Systems and Management
  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Control and Optimization

Cite this

Ibeid, H., Meng, S., Dobon, O., Olson, L., & Gropp, W. (2019). Learning with analytical models. In Proceedings - 2019 IEEE 33rd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2019 (pp. 778-786). [8778229] (Proceedings - 2019 IEEE 33rd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IPDPSW.2019.00128