Tree partition based parallel frequent pattern mining on shared memory systems. - Dehao Chen; Chunrong Lai; Wei Hu 002; Wenguang Chen; Yimin Zhang 002; Weimin Zheng (IPDPS, 2006)

MapCG: writing parallel program portable between CPU and GPU. - Chuntao Hong; Dehao Chen; Wenguang Chen; Weimin Zheng; Haibo Lin (PACT, 2010)

Taming hardware event samples for FDO compilation. - Dehao Chen; Neil Vachharajani; Robert Hundt; Shih-wei Liao; Vinodha Ramasamy; Paul Yuan; Wenguang Chen; Weimin Zheng (CGO, 2010)

CUDA-Zero: a framework for porting shared memory GPU applications to multi-GPUs. - Dehao Chen; Wenguang Chen; Weimin Zheng (Sci. China Inf. Sci., 2012)

Providing Source Code Level Portability Between CPU and GPU with MapCG. - Chun-Tao Hong; Dehao Chen; Yubei Chen; Wen-Guang Chen; Wei-Min Zheng; Haibo Lin (J. Comput. Sci. Technol., 2012)

Taming Hardware Event Samples for Precise and Versatile Feedback Directed Optimizations. - Dehao Chen; Neil Vachharajani; Robert Hundt; Xinliang D. Li; Stéphane Eranian; Wenguang Chen; Weimin Zheng (IEEE Trans. Computers, 2013)

Hardware Counted Profile-Guided Optimization. - Baptiste Wicht; Roberto A. Vitillo; Dehao Chen; David Levinthal (CoRR, 2014)

AutoFDO: automatic feedback-directed optimization for warehouse-scale applications. - Dehao Chen; Xinliang David Li; Tipp Moseley (CGO, 2016)

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism. - Yanping Huang; Yonglong Cheng; Dehao Chen; HyoukJoong Lee; Jiquan Ngiam; Quoc V. Le; Zhifeng Chen (CoRR, 2018)

Image Classification at Supercomputer Scale. - Chris Ying; Sameer Kumar; Dehao Chen; Tao Wang; Youlong Cheng (CoRR, 2018)

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism. - Yanping Huang; Youlong Cheng; Ankur Bapna; Orhan Firat; Dehao Chen; Mia Xu Chen; HyoukJoong Lee; Jiquan Ngiam; Quoc V. Le; Yonghui Wu; Zhifeng Chen (NeurIPS, 2019)

Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling. - Jonathan Shen; Patrick Nguyen; Yonghui Wu; Zhifeng Chen; Mia Xu Chen; Ye Jia; Anjuli Kannan; Tara N. Sainath; Yuan Cao 007; Chung-Cheng Chiu; Yanzhang He; Jan Chorowski; et al. (CoRR, 2019)

Scale MLPerf-0.6 models on Google TPU-v3 Pods. - Sameer Kumar; Victor Bitorff; Dehao Chen; Chiachen Chou; Blake A. Hechtman; HyoukJoong Lee; Naveen Kumar; Peter Mattson; Shibo Wang; Tao Wang; Yuanzhong Xu; Zongwei Zhou (CoRR, 2019)

MLPerf Training Benchmark. - Peter Mattson; Christine Cheng; Cody Coleman; Greg Diamos; Paulius Micikevicius; David A. Patterson 001; Hanlin Tang; Gu-Yeon Wei; Peter Bailis; Victor Bittorf; David Brooks 001; Dehao Chen; et al. (CoRR, 2019)

MLPerf Training Benchmark. - Peter Mattson; Christine Cheng; Gregory F. Diamos; Cody Coleman; Paulius Micikevicius; David A. Patterson 001; Hanlin Tang; Gu-Yeon Wei; Peter Bailis; Victor Bittorf; David Brooks 001; Dehao Chen; et al. (MLSys, 2020)

Automatic Cross-Replica Sharding of Weight Update in Data-Parallel Training. - Yuanzhong Xu; HyoukJoong Lee; Dehao Chen; Hongjun Choi; Blake A. Hechtman; Shibo Wang (CoRR, 2020)

Exploring the limits of Concurrency in ML Training on Google TPUs. - Sameer Kumar; James Bradbury; Cliff Young; Yu Emma Wang; Anselm Levskaya; Blake A. Hechtman; Dehao Chen; HyoukJoong Lee; Mehmet Deveci; Naveen Kumar; Pankaj Kanwar; Shibo Wang; et al. (CoRR, 2020)

GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding. - Dmitry Lepikhin; HyoukJoong Lee; Yuanzhong Xu; Dehao Chen; Orhan Firat; Yanping Huang; Maxim Krikun; Noam Shazeer; Zhifeng Chen (ICLR, 2021)

Exploring the Limits of Concurrency in ML Training on Google TPUS. - Sameer Kumar; Yu Emma Wang; Cliff Young; James Bradbury; Naveen Kumar; Dehao Chen; Andy Swing (MLSys, 2021)

GSPMD: General and Scalable Parallelization for ML Computation Graphs. - Yuanzhong Xu; HyoukJoong Lee; Dehao Chen; Blake A. Hechtman; Yanping Huang; Rahul Joshi; Maxim Krikun; Dmitry Lepikhin; Andy Ly; Marcello Maggioni; Ruoming Pang; Noam Shazeer; et al. (CoRR, 2021)

News Recommendation Model Based on Long-Term and Short-Term Interests. - Lei Ren; Dehao Chen; Zexin Miao; Huisheng Zhang (ICBDT, 2022)

LaMDA: Language Models for Dialog Applications. - Romal Thoppilan; Daniel De Freitas; Jamie Hall; Noam Shazeer; Apoorv Kulshreshtha; Heng-Tze Cheng; Alicia Jin; Taylor Bos; Leslie Baker; Yu Du; YaGuang Li; Hongrae Lee; et al. (CoRR, 2022)

Overlap Communication with Dependent Computation via Decomposition in Large Deep Learning Models. - Shibo Wang; Jinliang Wei; Amit Sabne; Andy Davis; Berkin Ilbeyi; Blake Hechtman; Dehao Chen; Karthik Srinivasa Murthy; Marcello Maggioni; Qiao Zhang; Sameer Kumar; Tongfei Guo; et al. (ASPLOS (1), 2023)

Quaternion Extreme Learning Machine Based on Real Augmented Representation. - Huisheng Zhang; Zaiqiang Wang; Dehao Chen; Shuai Zhu; Dongpo Xu (IEEE Signal Process. Lett., 2023)