Prediction of Distributed Ultrasound Simulation Execution Time Using Machine Learning

This study introduces a comprehensive system de-signed 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 performan...

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Bibliographic Details
Published in2024 IEEE Congress on Evolutionary Computation (CEC) pp. 1 - 8
Main Authors Jaros, Jiri, Jaros, Marta, Buchta, Martin
Format Conference Proceeding
LanguageEnglish
Published IEEE 30.06.2024
Subjects
Adaptation models
Computational modeling
Estimation
HeuristicLab
k-Wave
Load modeling
Machine learning
Moldable tasks
Neural Network
Neural networks
Prediction of Execution Time
Predictive models
Resource management
Simulation
Supercomputer
Symbolic Regression
Ultrasonic imaging
Ultrasound
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Summary:This study introduces a comprehensive system de-signed 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 Iarge-scale simulation executions.
DOI:10.1109/CEC60901.2024.10611947