Publication Details
Estimation of Distributed Ultrasound Simulation Execution Time Using Machine Learning
Prediction of Execution Time, Moldable tasks, Symbolic Regression, Neural
Network, Supercomputer, Simulation, k-Wave, Ultrasound, HeuristicLab.
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.
@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/"
}