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.

翻译：本文利用高性能计算(HPC)技术对卷积神经网络(CNN)算法进行优化。通过采用多核处理器、GPU以及OpenMPI和CUDA等并行计算框架，加速CNN模型的训练过程。该方法在性能提升和训练时间缩短方面均优于其他策略。研究表明，HPC技术能够有效改进CNN方法，从而实现大规模CNN模型更快速、更精确的训练。