TY - GEN
T1 - Deep Gaussian process with multitask and transfer learning for performance optimization
AU - Sid-Lakhdar, Wissam M.
AU - Aznaveh, Mohsen
AU - Luszczek, Piotr
AU - Dongarra, Jack
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85142272791&partnerID=8YFLogxK
U2 - 10.1109/HPEC55821.2022.9926396
DO - 10.1109/HPEC55821.2022.9926396
M3 - Conference contribution
AN - SCOPUS:85142272791
T3 - 2022 IEEE High Performance Extreme Computing Conference, HPEC 2022
BT - 2022 IEEE High Performance Extreme Computing Conference, HPEC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE High Performance Extreme Computing Conference, HPEC 2022
Y2 - 19 September 2022 through 23 September 2022
ER -