TY  - JOUR
T1  - A Parallel Processing Scheme on TensorFlow for Improving Training and Validation Performance
AU  - Choi, Jinseo 
AU  - Kang, Donghyun 
JO  - Journal of KIISE, JOK
PY  - 2022
DA  - 2022/1/14
DO  - 10.5626/JOK.2022.49.6.407
KW  - Multi-threads
KW  - deep learning
KW  - TensorFlow
KW  - GPU and CPU utilization
AB  - 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.