## 3 Results  
To carry out the processing required for this research, we used three nodes: the master node and two computing nodes. The load balancing of the processes on each node was performed according to Horovod documentation [37], using the _horovodrun_ command to distribute processes between nodes. It is crucial to have extensive knowledge of the server configuration and application version control as each component must complement each other to function correctly. Developing a high-performance and efficient system with high computational power demands a significant investment of resources and capital. However, even with limited computational capabilities, the HPCC was able to implement the CNN models for the analysis and processing of images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Device** & **Number of Cores** & **Base Clock Speed** & **FLOPS/Cycle** & **Total FLOPS** \\ \hline Intel i7 7700K & 4 & 4.2 GHz & 16 & 268.8 GFLOPS \\ Nvidia GTX 1050 ti & 768 & 1290 MHz & 2 & 1981.4 GFLOPS \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics of the computers used to implement the proposed HPCC.  
Results Obtained by Applying the CNN InceptionV3 and VGG16 Models  
The CNN models used for this research, InceptionV3 and VGG16, were pretrained to classify 1000 categories of objects using the ImageNet dataset [46]. This image dataset was created as a resource for researchers, teachers, and students dedicated to the development of sophisticated algorithms capable of indexing, retrieving, and organizing and, in the case of CNNs, classifying multimedia images obtained from the Web.  
Our choice of models was based solely on testing the capacity of the HPCC in applications with CNNs. The metric used to evaluate the CNN models was accuracy, which allowed for the recognition of the best performance based on how close a measured value was to the true value.  
To test the level of computational processing, the number of processes executed by the HPCC and the time it took to carry out the processing were considered. A loss function (or optimization function or objective function) was used during training, cross entropy or categorical_crossentropy as named in Keras, which allowed defining a feedback signal during training. In total, twelve processes were executed in the HPCC, four for each processor or computing node.  
While the computational level of the HPCC utilized in this research may not have been very high, the results obtained from implementing the CNN InceptionV3 and VGG16 models showcased the potential of the system. The physical differences between nodes, as shown in Figure 4, did not hinder the overall performance of the system, as their architecture was similar, as described in Table 2.  
The evaluation of the infrastructure was based on several key measures, as shown in the tables below. Tables 4 and 5 present the execution time of the CNN scripts used for the experimentation, where each execution was carried out with a different number of processes to determine the optimal configuration. For the InceptionV3 model, the best execution time was achieved with six processes, while the best accuracy was obtained with eight processes. Similarly, the VGG16 model performed best for the execution time with six processes and for the accuracy with one process.  
possible to take advantage of the twelve processes in total that were available for processing. Even so, it can be noted that the InceptionV3 model had a higher accuracy but less processing time, while the VGG16 model had a better processing time but lower accuracy.  
Overall, the InceptionV3 model exhibited the best processing time, as shown in Figure 7, indicating its superior performance in infrastructures such as the one proposed in this project. By using the InceptionV3 model, we achieved a processing time of 37 min and 55.193 s with an accuracy of 0.65 as shown in Figure 8.