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Detail publikace
JAROŠ, J. JAROŠ, M. BUCHTA, M.
Originální název
Estimation of Distributed Ultrasound Simulation Execution Time Using Machine Learning
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
This study introduces a comprehensive system designed to predict the execution time of k-Wave ultrasound simulations, factoring in the domain size and allocated computing resources. The predictive models, developed using symbolic regression and neural networks, were trained on historical performance data acquired from the Barbora supercomputer. For domain sizes with optimal parameters, the symbolic regression model outperformed, achieving an average error of 5.64%. Conversely, the neural network showed commendable efficacy in general domain scenarios, with an average error of 8.25%. Notably, in both instances, the average error remained below the 10% threshold, aligning closely with the uncertainty inherent in the measured data and the execution of real large-scale jobs. Consequently, this predictive system is well-suited for deployment in resource optimization frameworks, significantly enhancing the efficiency of large-scale simulation executions.
Klíčová slova
Prediction of Execution Time, Moldable tasks, Symbolic Regression, Neural Network, Supercomputer, Simulation, k-Wave, Ultrasound, HeuristicLab.
Autoři
JAROŠ, J.; JAROŠ, M.; BUCHTA, M.
Vydáno
8. 8. 2024
Nakladatel
Institute of Electrical and Electronics Engineers
Místo
Yokohama
ISBN
979-8-3503-0836-5
Kniha
2024 IEEE Congress on Evolutionary Computation (CEC)
Strany od
1
Strany do
8
Strany počet
URL
https://www.fit.vut.cz/research/publication/13130/
BibTex
@inproceedings{BUT189527, author="Jiří {Jaroš} and Marta {Jaroš} and Martin {Buchta}", title="Estimation of Distributed Ultrasound Simulation Execution Time Using Machine Learning", booktitle="2024 IEEE Congress on Evolutionary Computation (CEC)", year="2024", pages="1--8", publisher="Institute of Electrical and Electronics Engineers", address="Yokohama", doi="10.1109/CEC60901.2024.10611947", isbn="979-8-3503-0836-5", url="https://www.fit.vut.cz/research/publication/13130/" }