<![CDATA[Background: Large-scale machine learning models require substantial computational resources during training, particularly in GPU-intensive and distributed computing environments. Although modern training pipelines achieve high predictive performance, they often rely on static resource allocation strategies that do not adapt to evolving learning dynamics. This leads to inefficient GPU utilization, increased training time, and unnecessary computational cost, limiting the scalability and sustainability of large-scale AI systems.Aims: This study aims to improve training efficiency by proposing a Dynamic Resource Allocation (DRA) framework that integrates real-time learning signals into computational resource management. The framework dynamically adjusts GPU allocation based on convergence behavior, enabling efficient alignment between computational demand and model training stages.Methods: The proposed framework employs a descriptive, analytical, and comparative experimental design, using secondary, confidential machine learning training logs comprising 1,500 training runs. The system integrates a training-monitoring module, a convergence-analysis mechanism, and an adaptive resource controller. Performance evaluation is conducted by comparing static allocation and dynamic allocation strategies using metrics such as training time, GPU utilization, model accuracy, and computational efficiency.Results: Experimental results demonstrate that the proposed framework significantly improves training efficiency. The dynamic allocation strategy reduces training time by approximately 30–32%, increases GPU utilization by up to 17%, and improves overall computational efficiency without degrading model accuracy. Furthermore, the convergence analysis shows that the proposed method achieves faster, more stable convergence than static allocation strategies.Conclusion: The findings confirm that integrating training-aware resource allocation into machine learning pipelines significantly enhances both efficiency and sustainability. By dynamically aligning computational resources with learning behavior, the proposed framework reduces wasteful computation while maintaining predictive performance. This approach provides a scalable solution for efficient large-scale model training in cloud and high-performance computing environments.]]>
Copyrights © 2026
