## 2 Related Works  
In recent years, various strategies have been explored to accelerate the training process of DL models. One of the most widely accepted approaches is parallel computing. Numerous studies have shown that incorporating HPC into AI significantly reduces training times and yields more accurate results, especially when handling large-scale data. In medical investigations, HPC is very useful, as shown in the work of [13], in which a modular pipeline technique is presented to detect tumor regions in digital specimens of breast lymph nodes using DL models. They showed that this technique is applicable to both local machines and HPC resources using containers. Additionally, they mentioned that distribution training was carried out using four NVIDIA K80 GPUs and a single NVIDIA V100 GPU. In the field of hyperspectral remote sensing, [PERSON] et al. [14] conducted a study that addressed the computational challenges that arise in hyperspectral remote sensing applications due to the high dimensionality of the data generated by modern sensors. This article explores HPC techniques to improve the efficiency and accuracy of the processing and analysis of these data. Exact sciences, such as physics, have also greatly benefited from the use of HPC. For example, the work by [PERSON] [15] analyzed the existing computational methods for high-energy physics (HEP) from two perspectives: numerical methods and HPC. This article highlights the importance of stable simulations and numerical methods for high-precision analysis, experimental validation, and visualization in HEP. Some of the techniques mentioned and simulated were Monte Carlo, Markovian Monte Carlo, the overflow method in particle physics, kernel estimation in HEP, and random matrix theory. Furthermore, within mathematics, [PERSON] et al. [16] highlighted the importance of implementing HPC to solve complex mathematical problems that would be intractable using traditional methods. Beyond infrastructure-level comparisons, it is important to consider the evolution of deep learning applications in emerging fields that demand computational efficiency and adaptability. For instance, [PERSON] et al. [17] presented a novel self-supervised graph contrastive learning method for structured data, demonstrating how deep learning models can be optimized using positive sample selection strategies. Specifically, in the performance evaluation of DS, multiple studies have implemented DDL frameworks to reinforce the theory that using HPC improves the accuracy of AI task results. Table 1 lists the main studies that inspired this study. Compared to the studies mentioned in Table 1, the approach presented in this study offers several advantages that make it stand out. While most related works are based on high-end HPC infrastructures with large-scale distributed systems (often exceeding four nodes) [9; 20; 21], our study demonstrates that it is feasible to achieve competitive training performance and acceptable validation metrics using a low-scalability distributed system composed of outdated hardware. This significantly reduces the cost barrier and promotes sustainability by repurposing existing hardware for AI tasks, which are critical to the needs of today. However, a notable limitation is the limited generalization to larger-scale applications, as our infrastructure may not support the computational demands of more complex models or large-scale datasets. Furthermore, while many related studies focus exclusively on processing time or model accuracy, this study offers a balanced