Deep Gaussian process with multitask and transfer learning for performance optimization

Wissam M. Sid-Lakhdar; Mohsen Aznaveh; Piotr Luszczek; Jack Dongarra

doi:10.1109/HPEC55821.2022.9926396

Deep Gaussian process with multitask and transfer learning for performance optimization

Wissam M. Sid-Lakhdar, Mohsen Aznaveh, Piotr Luszczek, Jack Dongarra

Computer Science and Mathematics Div

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

We combine Deep Gaussian Processes with multitask and transfer learning for the performance modeling and optimization of HPC applications. Deep Gaussian processes merge the uncertainty quantification advantage of Gaussian Processes with the predictive power of deep learning. Multitask and transfer learning allow for improved learning efficiency when several similar tasks are to be learned simultaneously and when previous learned models are sought to help in the learning of new tasks, respectively. A comparison with state-of-the-art autotuners shows the advantage of our approach on two application problems.

Original language	English
Title of host publication	2022 IEEE High Performance Extreme Computing Conference, HPEC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665497862
DOIs	https://doi.org/10.1109/HPEC55821.2022.9926396
State	Published - 2022
Event	2022 IEEE High Performance Extreme Computing Conference, HPEC 2022 - Virtual, Online, United States Duration: Sep 19 2022 → Sep 23 2022

Publication series

Name	2022 IEEE High Performance Extreme Computing Conference, HPEC 2022

Conference

Conference	2022 IEEE High Performance Extreme Computing Conference, HPEC 2022
Country/Territory	United States
City	Virtual, Online
Period	09/19/22 → 09/23/22

Funding

This work was supported by the U.S. Department of Energy, Office of Science, ASCR under Award Number DE-SC0021419 the National Science Foundation under OAC grant No. 2004541. This work was supported by the U.S. Department of Energy, Office of Science, ASCR under Award Number DE-SC0021419 the National Science Foundation under OAC grant No. 2004541

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10.1109/HPEC55821.2022.9926396

Cite this

Sid-Lakhdar, W. M., Aznaveh, M., Luszczek, P., & Dongarra, J. (2022). Deep Gaussian process with multitask and transfer learning for performance optimization. In 2022 IEEE High Performance Extreme Computing Conference, HPEC 2022 (2022 IEEE High Performance Extreme Computing Conference, HPEC 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/HPEC55821.2022.9926396

@inproceedings{f3304b17c2404cfcb0aaa65e74d8495d,

title = "Deep Gaussian process with multitask and transfer learning for performance optimization",

abstract = "We combine Deep Gaussian Processes with multitask and transfer learning for the performance modeling and optimization of HPC applications. Deep Gaussian processes merge the uncertainty quantification advantage of Gaussian Processes with the predictive power of deep learning. Multitask and transfer learning allow for improved learning efficiency when several similar tasks are to be learned simultaneously and when previous learned models are sought to help in the learning of new tasks, respectively. A comparison with state-of-the-art autotuners shows the advantage of our approach on two application problems.",

author = "Sid-Lakhdar, {Wissam M.} and Mohsen Aznaveh and Piotr Luszczek and Jack Dongarra",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE High Performance Extreme Computing Conference, HPEC 2022 ; Conference date: 19-09-2022 Through 23-09-2022",

year = "2022",

doi = "10.1109/HPEC55821.2022.9926396",

language = "English",

series = "2022 IEEE High Performance Extreme Computing Conference, HPEC 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2022 IEEE High Performance Extreme Computing Conference, HPEC 2022",

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Sid-Lakhdar, WM, Aznaveh, M, Luszczek, P & Dongarra, J 2022, Deep Gaussian process with multitask and transfer learning for performance optimization. in 2022 IEEE High Performance Extreme Computing Conference, HPEC 2022. 2022 IEEE High Performance Extreme Computing Conference, HPEC 2022, Institute of Electrical and Electronics Engineers Inc., 2022 IEEE High Performance Extreme Computing Conference, HPEC 2022, Virtual, Online, United States, 09/19/22. https://doi.org/10.1109/HPEC55821.2022.9926396

Deep Gaussian process with multitask and transfer learning for performance optimization. / Sid-Lakhdar, Wissam M.; Aznaveh, Mohsen; Luszczek, Piotr et al.
2022 IEEE High Performance Extreme Computing Conference, HPEC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE High Performance Extreme Computing Conference, HPEC 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - We combine Deep Gaussian Processes with multitask and transfer learning for the performance modeling and optimization of HPC applications. Deep Gaussian processes merge the uncertainty quantification advantage of Gaussian Processes with the predictive power of deep learning. Multitask and transfer learning allow for improved learning efficiency when several similar tasks are to be learned simultaneously and when previous learned models are sought to help in the learning of new tasks, respectively. A comparison with state-of-the-art autotuners shows the advantage of our approach on two application problems.

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BT - 2022 IEEE High Performance Extreme Computing Conference, HPEC 2022

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Sid-Lakhdar WM, Aznaveh M, Luszczek P, Dongarra J. Deep Gaussian process with multitask and transfer learning for performance optimization. In 2022 IEEE High Performance Extreme Computing Conference, HPEC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 IEEE High Performance Extreme Computing Conference, HPEC 2022). doi: 10.1109/HPEC55821.2022.9926396

Deep Gaussian process with multitask and transfer learning for performance optimization

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