  This paper optimizes the Convolutional Neural Network (CNN) algorithm using
high-performance computing (HPC) technologies. It uses multi-core processors,
GPUs, and parallel computing frameworks like OpenMPI and CUDA to speed up CNN
model training. The approach improves performance and training time and is
superior to alternative strategies. The study demonstrates how HPC technologies
can refine the CNN method, resulting in faster and more accurate training of
large-scale CNN models.
