https://hdl.handle.net/10481/45175 CASIP 2026-04-18T06:23:12Z https://hdl.handle.net/10481/103794 Energy-time Modelling of Distributed Multi-population Genetic Algorithms with Dynamic Workload in HPC Clusters Escobar Pérez, Juan José; Sánchez-Cuevas, Pablo; Prieto Campos, Beatriz; Savran Kiziltepe, Rukiye; Díaz-del-Río, Fernando; Kimovski, Dragi Time and energy efficiency is a highly relevant objective in high-performance computing systems, with high costs for executing the tasks. Among these tasks, evolutionary algorithms are of consideration due to their inherent parallel scalability and usually costly fitness evaluation functions. In this respect, several scheduling strategies for workload balancing in heterogeneous systems have been proposed in the literature, with runtime and energy consumption reduction as their goals. Our hypothesis is that a dynamic workload distribution can be fitted with greater precision using metaheuristics, such as genetic algorithms, instead of linear regression. Therefore, this paper proposes a new mathematical model to predict the energy-time behaviour of applications based on multi-population genetic algorithms, which dynamically distributes the evaluation of individuals among the CPU-GPU devices of heterogeneous clusters. An accurate predictor would save time and energy by selecting the best resource set before running such applications. The estimation of the workload distributed to each device has been carried out by simulation, while the model parameters have been fitted in a two-phase run using another genetic algorithm and the experimental energy-time values of the target application as input. When the new model is analysed and compared with another based on linear regression, the one proposed in this work significantly improves the baseline approach, showing normalised prediction errors of 0.081 for runtime and 0.091 for energy consumption, compared to 0.213 and 0.256 shown in the baseline approach. PID2022-137461NB-C32 and PID2023-151065OB-I00 projects, funded by the MICIU/AEI/10.13039/501100011033 and by ESF+ (“NextGenerationEU/PRTR”). PPJIA2023-025 project, funded by the University of Granada. Program of mobility stays for professors and researchers in foreign higher education and research centres, funded by the Spanish Ministry of Universities under grant CAS22/00332. P.S.-C. was supported by “Predoctores 2021” (PREDOC_01229) fellowship from the Ministry of Economic Transformation, Industry, Knowledge and Universities of the Regional Government of Andalusia . https://hdl.handle.net/10481/96724 Evolution of Computing Energy Efficiency: Koomey’s Law Revisited Prieto Espinosa, Alberto; Prieto Campos, Beatriz; Escobar Pérez, Juan José; Lampert, Thomas For information and communication technology power consumption to be sustainable, the energy efficiency of computing systems must grow at least as fast as the demand for computing services. It is therefore crucial to understand how energy efficiency is evolving and how it will trend in the future, in order to take appropriate measures where possible. This article analyses the evolution of this parameter by analysing high-performance computers from 2008 to 2023, contrasting the results with those from Koomey’s Law. It is concluded, after comparing the two that in the studied period and in the near future, energy efficiency continues to grow exponentially but at a slower rate than that established by Koomey’s Law (maximum energy efficiency doubles every 2.29 years instead of every 1.57 years). Another interesting result is that energy efficiency grows at a slower rate (doubling every 2.29 years) than performance (doubling every 1.85 years). This work was partially supported by Grant PID2022-137461NB-C31 funded by MICIU/AEI/10.13039/501100011033 and by “ERDF/EU”, Grant PID2022-137461NB-C32 funded by MICIU/AEI/10.13039/501100011033 and by “ERDF/EU”, and Project PPJIA2023-025 funded by the University of Granada (Spain). https://hdl.handle.net/10481/92001 Energy-aware KNN for EEG Classification: A Case Study in Heterogeneous Platforms Escobar Pérez, Juan José; Rodríguez, Francisco; Savran Kiziltepe, Rukiye; Prieto Campos, Beatriz; Kimovski, Dragi; Ortiz, Andrés; Damas Hermoso, Miguel The growing energy consumption caused by IT is forcing application developers to consider energy efficiency as one of the fundamental design parameters. This parameter acquires great relevance in HPC systems when running artificial neural networks and Machine Learning applications. Thus, this article shows an example of how to estimate and consider energy consumption in a real case of EEG classification. An efficient and distributed implementation of the KNN algorithm that uses mRMR as a feature selection technique to reduce the dimensionality of the dataset is proposed. The performance of three different workload distributions is analyzed to identify which one is more suitable according to the experimental conditions. The proposed approach outperforms the classification results obtained by previous works. It achieves an accuracy rate of 88.8% and a speedup of 74.53 when running on a multi-node heterogeneous cluster, consuming only 13.38% of the energy of the sequential version. https://hdl.handle.net/10481/90168 Vampire: A Smart Energy Meter for Synchronous Monitoring in a Distributed Computer System Díaz García, Antonio Francisco; Prieto Campos, Beatriz; Escobar Pérez, Juan José; Lampert, Thomas This paper presents the design and implementation of a low-cost system oriented to the synchronised and real-time surveillance and monitoring of electrical parameters of different computer devices. To measure energy consumption in a computer system, it is proposed to use, instead of a general-purpose wattmeter, one designed ad-hoc and synchronously collects the energy consumption of its various nodes or devices. The implementation of the devised system is based on the confluence of several technologies or tools widely used in the Internet of Things. Thus, this article the intelligent objects are the power meters, whose connections are based on the low-cost ESP32 microcontroller. The message transmission between devices is carried out with the standard message queuing telemetry transport (MQTT) protocol, the measurements are grouped in a database on an InfluxDB server that store the sensor data as time series, and Grafana is used as a graphical user interface. The efficiency of the proposed energy monitoring system is demonstrated by the experimental results of a real application that successfully and synchronously records the voltage, current, active power and cumulative energy consumption of a distributed cluster that includes a total of 60 cores. https://hdl.handle.net/10481/89102 Integration of Feature and Decision Fusion with Deep Learning Architectures for Video Classification Savran Kiziltepe, Rukiye; Gan, John Q.; Escobar Pérez, Juan José Information fusion is frequently employed to integrate diverse inputs, including sensory data, features, or decisions, in order to leverage the advantageous relationships among various features and classifiers. This paper presents a novel approach for video classification using deep learning architectures, including ConvLSTM and vision transformer based fusion architectures, which incorporates the combination of spatial and temporal features, along with the utilisation of decision fusion at multiple levels. The proposed vision transformer based method uses a 3D CNN to extract spatio-temporal information and different attention mechanisms to pay attention to essential features for action recognition and thus learns spatio-temporal dependencies effectively. The effectiveness of the methods proposed in this paper is validated through empirical evaluations conducted on two well-known video classification datasets, namely UCF-101 and KTH. The experimental findings indicate that the utilisation of both spatial and temporal features is essential, with the superior performance gained by using temporal features as the primary source of features in conjunction with two types of distinct spatial features when compared to other configurations. The multi-level decision fusion approach proposed in this study produces results comparable to those of feature fusion methods while offering the advantage of reduced memory requirements and computational costs. The fusion of RGB, HOG, and optical flow representations has demonstrated the best performance compared to other fusion methods examined in this study. It has also been demonstrated that the vision transformer based approaches significantly outperformed the ConvLSTM based approaches. Furthermore, an ablation study was conducted to compare the performances of vision transformer based feature fusion approaches for enhancing the performance of video classification.