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The Effect of High Performance Computer on Deep Neural Network

In recent years, convolutional neural networks (CNNs) have been extended widely to a large number of computer vision applications such as image classification, image detection, image segmentation, etc. In this paper, the three image processing applications are implemented by integrating with CNNs and the high performance computing (HPC) systems. To observe the performance of HPC, three CNN models for each image processing application have been trained with different values of parameters and their training times are provided in results. Four computing systems, Google collaboratory with central processing unit (CPU), Google collaboratory with graphics processing unit (GPU), Google Cloud with HPC, and The extreme science and engineering discovery environment (XSEDE) with HPC are compared in the work. The training program is suggested to use the parallel algorithm when GPU is available. This project explores that the HPC with GPU has the highest work efficiency regarding operating time.