20.500.12556/DKUM-92991 High-performance deployment operational Data analytics of pre-trained multi-label classification architectures with differential-evolution-based hyperparameter optimization (AutoDEHypO) This article presents a high-performance-computing differential-evolution-based hyperparameter optimization automated workflow (AutoDEHypO), which is deployed on a petascale supercomputer and utilizes multiple GPUs to execute a specialized fitness function for machine learning (ML). The workflow is designed for operational analytics of energy efficiency. In this differential evolution (DE) optimization use case, we analyze how energy efficiently the DE algorithm performs with different DE strategies and ML models. The workflow analysis considers key factors such as DE strategies and automated use case configurations, such as an ML model architecture and dataset, while monitoring both the achieved accuracy and the utilization of computing resources, such as the elapsed time and consumed energy. While the efficiency of a chosen DE strategy is assessed based on a multi-label supervised ML accuracy, operational data about the consumption of resources of individual completed jobs obtained from a Slurm database are reported. To demonstrate the impact on energy efficiency, using our analysis workflow, we visualize the obtained operational data and aggregate them with statistical tests that compare and group the energy efficiency of the DE strategies applied in the ML models. high-performance computing operational data analytics energy efficiency machine learning AutoML differential avolution optimization visoko zmogljivo računalništvo analitika operativnih podatkov energetska učinovitost diferencialna evolucija optimizacija strojno učenje true true false MDPI Angleški jezik Slovenski jezik https://www.mdpi.com/2227-7390/13/10/1681 © 2025 by the authors Članek v reviji 2025-05-29 13:52:42 2025-05-29 13:52:43 2025-05-30 03:10:32 0000-00-00 00:00:00 2025 0 0 50 str. iss. 10, [article no.] 1681 Vol. 13 2025 0000-00-00 Zaloznikova NiDoloceno NiDoloceno 2025-05-18 2025-05-19 2025-05-20 004.4 237135619 10.3390/math13101681 2227-7390 mathematics-13-01681-v2_(1).pdf mathematics-13-01681-v2_(1).pdf 1 4CA12184440A61037C57DC6D595F7312 5b6cb579495fc8a4f1e48a33b9b86a85f958aba42189383bee7348e083fee6bf c7a0bad6-3c83-11f0-b9c3-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=191502 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0