GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic Graphs

1 code implementation • 29 Nov 2023 • Yuchen Zhong, Guangming Sheng, Tianzuo Qin, Minjie Wang, Quan Gan, Chuan Wu

We introduce GNNFlow, a distributed framework that enables efficient continuous temporal graph representation learning on dynamic graphs on multi-GPU machines.

Graph Learning Graph Representation Learning +1

dPRO: A Generic Profiling and Optimization System for Expediting Distributed DNN Training

no code implementations • 5 May 2022 • Hanpeng Hu, Chenyu Jiang, Yuchen Zhong, Yanghua Peng, Chuan Wu, Yibo Zhu, Haibin Lin, Chuanxiong Guo

Distributed training using multiple devices (e. g., GPUs) has been widely adopted for learning DNN models over large datasets.

Compressed Communication for Distributed Training: Adaptive Methods and System

1 code implementation • 17 May 2021 • Yuchen Zhong, Cong Xie, Shuai Zheng, Haibin Lin

Recently, there has been a growing interest in using gradient compression to reduce the communication overhead of the distributed training.

Thin Structure Estimation with Curvature Regularization

no code implementations • ICCV 2015 • Dmitrii Marin, Yuri Boykov, Yuchen Zhong

Many applications in vision require estimation of thin structures such as boundary edges, surfaces, roads, blood vessels, neurons, etc.