41 citations · 51 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 3 cited
Understand Data Preprocessing for Effective End-to-End Training of Deep Neural Networks
Ping Gong, Yuxin Ma, Cheng Li +2
In this paper, we primarily focus on understanding the data preprocessing pipeline for DNN Training in the public cloud. First, we run experiments to test the performance implicati…
cs.DC2020★ 41 cited
HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism
Jay H. Park, Gyeongchan Yun, Chang M. Yi +5
Deep Neural Network (DNN) models have continuously been growing in size in order to improve the accuracy and quality of the models. Moreover, for training of large DNN models, the…
cs.DC2019★ 7 cited
Accelerated Training for CNN Distributed Deep Learning through Automatic Resource-Aware Layer Placement
Jay H. Park, Sunghwan Kim, Jinwon Lee +2
The Convolutional Neural Network (CNN) model, often used for image classification, requires significant training time to obtain high accuracy. To this end, distributed training is…