## 4 Results
### Performance Evaluation of Distributed Frameworks
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Infrastructure** & **Dataset** & \multicolumn{2}{c}{**Processing Time in Seconds**} & \multicolumn{2}{c}{**Speedup**} \\ \hline  & & **DDP PyTorch** & **Dist. Tensorflow** & **DDP PyTorch** & **Dist. Tensorflow** \\ \hline \multirow{3}{*}{Workstation} & Cloud & 15,142 & 19,098 & 15,142/10,935 = 1.38x & 19,098/13,028 = 1.47x \\  & MSLD & 734 & 1176 & 734/632 = 1.16x & 1176/2274 = 0.51x \\  & CIFAR 100 & 1448 & 2261 & 1448/6668 = 0.21x & 2261/652 = 3.47x \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Low- \\ Scalability \\ DS-HPC \\ \end{tabular} } & Cloud & 10,935 & 13,028 & \\  & MSLD & 632 & 2274 & \multicolumn{2}{c}{N/A} \\ \cline{1-1}  & CIFAR 100 & 6668 & 652 & \multicolumn{2}{c}{} \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Google \\ Calaboratory \\ \end{tabular} } & Cloud & 16,026 & 19,264 & 16,026/10,935 = 1.47x & 19,264/13,028 = 1.48x \\ \cline{1-1}  & MSLD & 1063 & 3156 & 1063/632 = 1.68x & 3156/2274 = 1.39x \\ \cline{1-1}  & CIFAR 100 & 2224 & 4393 & 2224/6668 = 0.03x & 4393/652 = 6.74x \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Speedup of the computing infrastructure. However, it is not enough to highlight the best scores; in DDL, it is also important to see the behavior or performance of the model when trained. For this, learning curve graphs were also obtained to visually determine whether the best score data were consistent with the model performance for each of the computing infrastructures used. Learning curves in DL models can help us graphically visualize the training process and identify patterns that can help us properly adjust or configure a CNN model. Figure 5 shows the learning curves obtained when training the ResNet50 model on the 38-Cloud dataset. The model showed progressive convergence, with a consistently decreasing training loss and validation accuracy reaching values close to 93% in the final epochs using Distributed Tensorflow. Compared to the learning curves for training in the Workstation and Google Colab, the fit is much better with only 17 epochs. However, as mentioned above, the best score is obtained using Google Colab with DDP PyTorch. From the graph, we can see that at least during the first 20 epochs, overfitting occurs, indicating a poor correlation between the training and validation data. This can also be observed with the Distributed Tensorflow implementation in both Google Colab and Workstation. These results indicate the good capacity of the model to adapt to the spectral and spatial characteristics of the multispectral dataset in the LSDS-HPC infrastructure. For the MSLD dataset (Figure 6), which represents a small and specialized set of clinical images, the model achieved high training and validation accuracies in a few epochs. The loss quickly stabilized, suggesting the rapid adaptation of the model to the visual characteristics of the dataset. This behavior reinforces the model's effectiveness in environments with balanced data and restricted domains. The DS-HPC infrastructure proved suitable for fast and efficient training on this type of dataset for both DDP PyTorch and Distributed Tensorflow since their learning curves are the most correlated. Although the accuracy and loss values were the lowest in the experiments, it demonstrated better agreement in its  
Figure 5: Learning curves of the 38-cloud dataset for accuracy and loss evaluations. graphical representation, unlike its training in Workstation and Google Colab, which, in some cases, showed overfitting between the training and validation data. In the case of the CIFAR100 dataset (Figure 7), characterized by high interclass variability and low resolution (32 \(\times\) 32), a more fluctuating behavior in the validation accuracy was observed, causing overfitting from its first iterations or epochs. Although the training loss was notably reduced, the accuracy stabilized at around 70% using DDP PyTorch, indicating generalization challenges in this dataset.