## 2 Related Works
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Approach** & **Highlights** \\ \hline [PERSON] et al. [9] & Optimization for heterogeneous multi-GPU clusters, using Tensorflow as a distributed processing framework, with the goal of improving resource utilization without sacrificing training accuracy. & Improving computational performance by reducing I/O bottlenecks and efficiently increasing resource utilization on heterogeneous multi-GPU clusters using Distributed Tensorflow. \\ \hline [PERSON] et al. [18] & Performance evaluation of DL tools (such as TensorFlow, Caffe, and MXNet) running in virtualized environments with Docker containers, compared to running directly on the host operating system. & The use of Docker containers generates minimal overhead in the training performance of DL models. Tests showed almost negligible performance differences between the native and containerized environments, and the evaluated tools maintained consistent and stable performance, validating their use in both production and experimental environments. \\ \hline [PERSON] et al. [19] & Comparison of five DL frameworks (Caffe, Neon, TensorFlow, Theano, and Torch) evaluating their extensibility, hardware utilization, and computational performance (training and execution times) on both CPUs and multithreaded GPUs, testing different architectures, including convolutional networks. & Performance was found to vary depending on the framework, network architecture, and hardware environment, so the choice of framework should consider these factors. Torch was the most efficient on both CPUs and GPUs and the most extensible. \\ \[PERSON] et al. [20] & Systematic comparison of DDL tools—specifically TensorFlow, MXNet, and CNTK—running on supercomputers. Their computational efficiency, scalability, and performance when training complex models in HPC environments are analyzed. & Provide a comprehensive evaluation of the performance of distributed tools on HPC architectures, considering both speed and scalability. Practical and comparable metrics such as training time, speedup, and computational resource monitoring are used to measure the efficiency of computational resource usage during DL model training. \\ \hline [PERSON] et al. [21] & An overview of parallel and DDL, addressing both the theoretical principles and practical challenges for efficiently deploying models on parallel infrastructures. It focuses on explaining the various parallelization techniques and their impact on training performance. & The balance between computation and communication is critical to achieving real improvements in speed and scalability. Data parallelism is most efficient for tasks where the model is small or moderately sized and is trained on large amounts of data. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recent studies on HPC performance validation. assessment that includes empirical speedup, model performance on datasets from different application areas, and hardware configuration limitations. Setting up an LSDS-HPC is difficult due to the incompatibility of current libraries with obsolete hardware. These considerations position our approach as a cost-effective alternative for academic and resource-constrained settings.