| Naslov: | High-performance deployment operational Data analytics of pre-trained multi-label classification architectures with differential-evolution-based hyperparameter optimization (AutoDEHypO) |
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| Avtorji: | ID Prica, Teo (Avtor)
ID Zamuda, Aleš (Avtor) |
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| Datoteke: |  mathematics-13-01681-v2_(1).pdf (1,61 MB)
MD5: 4CA12184440A61037C57DC6D595F7312
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| Jezik: | Angleški jezik |
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| Vrsta gradiva: | Članek v reviji |
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| Tipologija: | 1.01 - Izvirni znanstveni članek |
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| Organizacija: | FERI - Fakulteta za elektrotehniko, računalništvo in informatiko
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| Opis: | 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. |
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| Ključne besede: | high-performance computing, operational data analytics, energy efficiency, machine learning, AutoML, differential avolution, optimization |
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| Verzija publikacije: | Objavljena publikacija |
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| Poslano v recenzijo: | 18.05.2025 |
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| Datum sprejetja članka: | 19.05.2025 |
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| Datum objave: | 20.05.2025 |
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| Založnik: | MDPI |
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| Leto izida: | 2025 |
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| Št. strani: | 50 str. |
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| Številčenje: | Vol. 13, iss. 10, [article no.] 1681 |
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| PID: | 20.500.12556/DKUM-92991  |
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| UDK: | 004.4 |
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| COBISS.SI-ID: | 237135619 |
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| DOI: | 10.3390/math13101681 |
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| ISSN pri članku: | 2227-7390 |
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| Avtorske pravice: | © 2025 by the authors |
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| Datum objave v DKUM: | 29.05.2025 |
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| Število ogledov: | 0 |
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| Število prenosov: | 3 |
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| Metapodatki: |    |
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| Področja: | Ostalo
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