@article{M651A42E4,
title = "A Parallel Processing Scheme on TensorFlow for Improving Training and Validation Performance",
journal = "Journal of KIISE, JOK",
year = "2022",
issn = "2383-630X",
doi = "10.5626/JOK.2022.49.6.407",
author = "Jinseo Choi,Donghyun Kang",
keywords = "Multi-threads,deep learning,TensorFlow,GPU and CPU utilization",
abstract = "Most deep learning systems spend a lot of time on model training and validation. However, they sometimes tend to waste GPU and CPU resources because the pre-processing and batch processes based on a single thread result in a wait time. In this paper, we propose a new scheme that efficiently handles training and validation processes based on multi-threads. The proposed scheme can overlap the training and validation processes as much as possible by using a model copy operation that extends the processes with multi-threads. As a result, it improves the overall utilization of CPU and GPU. For evaluation, we implemented a convolutional neural network (CNN) using the TensorFlow framework. As a result, we clearly confirm that the proposed scheme saves the total training and validation time by up to 22.4% compared with the traditional schemes."
}