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2020 – today
- 2024
[c8]Xuanlei Zhao, Shenggan Cheng, Guangyang Lu, Haotian Zhou, Bin Jia, Yang You:
AutoChunk: Automated Activation Chunk for Memory-Efficient Deep Learning Inference. ICLR 2024- [c7]Xuanlei Zhao, Bin Jia, Haotian Zhou, Ziming Liu, Shenggan Cheng, Yang You:
HeteGen: Efficient Heterogeneous Parallel Inference for Large Language Models on Resource-Constrained Devices. MLSys 2024 - [c6]Jiangsu Du
, Jinhui Wei, Jiazhi Jiang, Shenggan Cheng, Dan Huang, Zhiguang Chen, Yutong Lu:
Liger: Interleaving Intra- and Inter-Operator Parallelism for Distributed Large Model Inference. PPoPP 2024: 42-54 - [c5]Shenggan Cheng, Xuanlei Zhao, Guangyang Lu, Jiarui Fang, Tian Zheng, Ruidong Wu, Xiwen Zhang, Jian Peng, Yang You:
FastFold: Optimizing AlphaFold Training and Inference on GPU Clusters. PPoPP 2024: 417-430 - [i9]Xuanlei Zhao, Shenggan Cheng, Guangyang Lu, Jiarui Fang, Haotian Zhou, Bin Jia, Ziming Liu, Yang You:
AutoChunk: Automated Activation Chunk for Memory-Efficient Long Sequence Inference. CoRR abs/2401.10652 (2024) - [i8]Xuanlei Zhao, Bin Jia, Haotian Zhou, Ziming Liu, Shenggan Cheng, Yang You:
HeteGen: Heterogeneous Parallel Inference for Large Language Models on Resource-Constrained Devices. CoRR abs/2403.01164 (2024) - [i7]Xuanlei Zhao, Shenggan Cheng, Zangwei Zheng, Zheming Yang, Ziming Liu, Yang You:
DSP: Dynamic Sequence Parallelism for Multi-Dimensional Transformers. CoRR abs/2403.10266 (2024) - [i6]Ziming Liu, Shaoyu Wang, Shenggan Cheng, Zhongkai Zhao, Xuanlei Zhao, James Demmel, Yang You:
WallFacer: Guiding Transformer Model Training Out of the Long-Context Dark Forest with N-body Problem. CoRR abs/2407.00611 (2024) - 2023
- [c4]Ziming Liu, Shenggan Cheng, Haotian Zhou, Yang You:
Hanayo: Harnessing Wave-like Pipeline Parallelism for Enhanced Large Model Training Efficiency. SC 2023: 56:1-56:13 - [i5]Shenggan Cheng, Ziming Liu, Jiangsu Du, Yang You:
ATP: Adaptive Tensor Parallelism for Foundation Models. CoRR abs/2301.08658 (2023) - [i4]Ziming Liu, Shenggan Cheng, Haotian Zhou, Yang You:
Hanayo: Harnessing Wave-like Pipeline Parallelism for Enhanced Large Model Training Efficiency. CoRR abs/2308.15762 (2023) - 2022
- [i3]Shenggan Cheng, Ruidong Wu, Zhongming Yu, Bin-Rui Li, Xiwen Zhang, Jian Peng, Yang You:
FastFold: Reducing AlphaFold Training Time from 11 Days to 67 Hours. CoRR abs/2203.00854 (2022) - 2021
- [c3]Bin-Rui Li, Shenggan Cheng, James Lin:
tcFFT: A Fast Half-Precision FFT Library for NVIDIA Tensor Cores. CLUSTER 2021: 1-11 - [i2]Bin-Rui Li, Shenggan Cheng, James Lin:
tcFFT: Accelerating Half-Precision FFT through Tensor Cores. CoRR abs/2104.11471 (2021) - 2020
- [j1]Zixuan Huang, Junming Fan, Shenggan Cheng, Shuai Yi, Xiaogang Wang, Hongsheng Li:
HMS-Net: Hierarchical Multi-Scale Sparsity-Invariant Network for Sparse Depth Completion. IEEE Trans. Image Process. 29: 3429-3441 (2020) - [c2]Shenggan Cheng, Hao-Ran Yu, Derek Inman, Qiucheng Liao, Qiaoya Wu, James Lin:
CUBE - Towards an Optimal Scaling of Cosmological N-body Simulations. CCGRID 2020: 685-690 - [c1]Kunyuan Du, Ya Zhang, Haibing Guan, Qi Tian, Yanfeng Wang, Shenggan Cheng, James Lin:
FTL: A Universal Framework for Training Low-Bit DNNs via Feature Transfer. ECCV (25) 2020: 700-716 - [i1]Shenggan Cheng, Hao-Ran Yu, Derek Inman, Qiucheng Liao, Qiaoya Wu, James Lin:
CUBE - Towards an Optimal Scaling of Cosmological N-body Simulations. CoRR abs/2003.03931 (2020)
Coauthor Index
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