1 code implementation • 13 Dec 2024 • Yudong Jiang, Baohan Xu, Siqian Yang, Mingyu Yin, Jing Liu, Chao Xu, Siqi Wang, Yidi Wu, Bingwen Zhu, Xinwen Zhang, Xingyu Zheng, Jixuan Xu, Yue Zhang, Jinlong Hou, Huyang Sun
Animation has gained significant interest in the recent film and TV industry.
no code implementations • Proceedings of the 2021 International Conference on Management of Data 2021 • Yidi Wu, Yuntao Gui, Tatiana Jin, James Cheng, Xiao Yan, Peiqi Yin, Yufei Cai, Bo Tang, Fan Yu
Graph neural networks (GNNs) have achieved remarkable performance in many graph analytics tasks such as node classification, link prediction and graph clustering.
no code implementations • Proceedings of the Sixteenth European Conference on Computer Systems 2021 • Yidi Wu, Kaihao Ma, Zhenkun Cai, Tatiana Jin, Boyang Li, Chenguang Zheng, James Cheng, Fan Yu
Graph neural networks (GNNs) have achieved breakthrough performance in graph analytics such as node classification, link prediction and graph clustering.
1 code implementation • Proceedings of the Sixteenth European Conference on Computer Systems 2021 • Zhenkun Cai, Xiao Yan, Yidi Wu, Kaihao Ma, James Cheng, Fan Yu
Graph neural networks (GNNs) have gained increasing popularity in many areas such as e-commerce, social networks and bio-informatics.
no code implementations • IEEE Transactions on Parallel and Distributed Systems 2021 • Yidi Wu, Kaihao Ma, Xiao Yan, Zhi Liu, Zhenkun Cai, Yuzhen Huang, James Cheng, Han Yuan, Fan Yu
We study how to support elasticity, that is, the ability to dynamically adjust the parallelism (i. e., the number of GPUs), for deep neural network (DNN) training in a GPU cluster.
1 code implementation • 16 Apr 2020 • Zhenkun Cai, Kaihao Ma, Xiao Yan, Yidi Wu, Yuzhen Huang, James Cheng, Teng Su, Fan Yu
A good parallelization strategy can significantly improve the efficiency or reduce the cost for the distributed training of deep neural networks (DNNs).