Dehao Chen - RankMe

25 papers A* 2A 3B 1Journal 15Unranked 4

Year	Rank	Type	Title / Venue / Authors
2023	—	`conf`	Overlap Communication with Dependent Computation via Decomposition in Large Deep Learning Models. ASPLOS (1) 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, Yuanzhong Xu, Zongwei Zhou
2023	J	`jnl`	Quaternion Extreme Learning Machine Based on Real Augmented Representation. IEEE Signal Process. Lett. Huisheng Zhang, Zaiqiang Wang, Dehao Chen, Shuai Zhu, Dongpo Xu
2022	J	`jnl`	LaMDA: Language Models for Dialog Applications. CoRR 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, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Kathleen S. Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Agüera y Arcas, Claire Cui, Marian Croak, Ed H. Chi, Quoc Le
2022	—	`conf`	News Recommendation Model Based on Long-Term and Short-Term Interests. ICBDT Lei Ren, Dehao Chen, Zexin Miao, Huisheng Zhang
2021	—	`conf`	Exploring the Limits of Concurrency in ML Training on Google TPUS. MLSys Sameer Kumar, Yu Emma Wang, Cliff Young, James Bradbury, Naveen Kumar, Dehao Chen, Andy Swing
2021	J	`jnl`	GSPMD: General and Scalable Parallelization for ML Computation Graphs. CoRR 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, Shibo Wang, Tao Wang, Yonghui Wu, Zhifeng Chen
2021	A*	`conf`	GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding. ICLR Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, Zhifeng Chen
2020	J	`jnl`	Automatic Cross-Replica Sharding of Weight Update in Data-Parallel Training. CoRR Yuanzhong Xu, HyoukJoong Lee, Dehao Chen, Hongjun Choi, Blake A. Hechtman, Shibo Wang
2020	J	`jnl`	Exploring the limits of Concurrency in ML Training on Google TPUs. CoRR 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, Skye Wanderman-Milne, Steve Lacy, Tao Wang, Tayo Oguntebi, Yazhou Zu, Yuanzhong Xu, Andy Swing
2020	J	`jnl`	GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding. CoRR Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, Zhifeng Chen
2020	—	`conf`	MLPerf Training Benchmark. MLSys Peter Mattson, Christine Cheng, Gregory F. Diamos, Cody Coleman, Paulius Micikevicius, David A. Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debo Dutta, Udit Gupta, Kim M. Hazelwood, Andy Hock, Xinyuan Huang, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia
2019	A*	`conf`	GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism. NeurIPS Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Xu Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, Zhifeng Chen
2019	J	`jnl`	Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling. CoRR Jonathan Shen, Patrick Nguyen, Yonghui Wu, Zhifeng Chen, Mia Xu Chen, Ye Jia, Anjuli Kannan, Tara N. Sainath, Yuan Cao, Chung-Cheng Chiu, Yanzhang He, Jan Chorowski, Smit Hinsu, Stella Laurenzo, James Qin, Orhan Firat, Wolfgang Macherey, Suyog Gupta, Ankur Bapna, Shuyuan Zhang, Ruoming Pang, Ron J. Weiss, Rohit Prabhavalkar, Qiao Liang, Benoit Jacob, Bowen Liang, HyoukJoong Lee, Ciprian Chelba, Sébastien Jean, Bo Li, Melvin Johnson, Rohan Anil, Rajat Tibrewal, Xiaobing Liu, Akiko Eriguchi, Navdeep Jaitly, Naveen Ari, Colin Cherry, Parisa Haghani, Otavio Good, Youlong Cheng, Raziel Alvarez, Isaac Caswell, Wei-Ning Hsu, Zongheng Yang, Kuan-Chieh Wang, Ekaterina Gonina, Katrin Tomanek, Ben Vanik, Zelin Wu, Llion Jones, Mike Schuster, Yanping Huang, Dehao Chen, Kazuki Irie, George F. Foster, John Richardson, Klaus Macherey, Antoine Bruguier, Heiga Zen, Colin Raffel, Shankar Kumar, Kanishka Rao, David Rybach, Matthew Murray, Vijayaditya Peddinti, Maxim Krikun, Michiel Bacchiani, Thomas B. Jablin, Robert Suderman, Ian Williams, Benjamin Lee, Deepti Bhatia, Justin Carlson, Semih Yavuz, Yu Zhang, Ian McGraw, Max Galkin, Qi Ge, Golan Pundak, Chad Whipkey, Todd Wang, Uri Alon, Dmitry Lepikhin, Ye Tian, Sara Sabour, William Chan, Shubham Toshniwal, Baohua Liao, Michael Nirschl, Pat Rondon
2019	J	`jnl`	MLPerf Training Benchmark. CoRR Peter Mattson, Christine Cheng, Cody Coleman, Greg Diamos, Paulius Micikevicius, David A. Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debojyoti Dutta, Udit Gupta, Kim M. Hazelwood, Andrew Hock, Xinyuan Huang, Bill Jia, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Guokai Ma, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia
2019	J	`jnl`	Scale MLPerf-0.6 models on Google TPU-v3 Pods. CoRR 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
2018	J	`jnl`	GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism. CoRR Yanping Huang, Yonglong Cheng, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Zhifeng Chen
2018	J	`jnl`	Image Classification at Supercomputer Scale. CoRR Chris Ying, Sameer Kumar, Dehao Chen, Tao Wang, Youlong Cheng
2016	A	`conf`	AutoFDO: automatic feedback-directed optimization for warehouse-scale applications. CGO Dehao Chen, Xinliang David Li, Tipp Moseley
2014	J	`jnl`	Hardware Counted Profile-Guided Optimization. CoRR Baptiste Wicht, Roberto A. Vitillo, Dehao Chen, David Levinthal
2013	J	`jnl`	Taming Hardware Event Samples for Precise and Versatile Feedback Directed Optimizations. IEEE Trans. Computers Dehao Chen, Neil Vachharajani, Robert Hundt, Xinliang D. Li, Stéphane Eranian, Wenguang Chen, Weimin Zheng
2012	J	`jnl`	CUDA-Zero: a framework for porting shared memory GPU applications to multi-GPUs. Sci. China Inf. Sci. Dehao Chen, Wenguang Chen, Weimin Zheng
2012	J	`jnl`	Providing Source Code Level Portability Between CPU and GPU with MapCG. J. Comput. Sci. Technol. Chun-Tao Hong, Dehao Chen, Yubei Chen, Wen-Guang Chen, Wei-Min Zheng, Haibo Lin
2010	B	`conf`	MapCG: writing parallel program portable between CPU and GPU. PACT Chuntao Hong, Dehao Chen, Wenguang Chen, Weimin Zheng, Haibo Lin
2010	A	`conf`	Taming hardware event samples for FDO compilation. CGO Dehao Chen, Neil Vachharajani, Robert Hundt, Shih-Wei Liao, Vinodha Ramasamy, Paul Yuan, Wenguang Chen, Weimin Zheng
2006	A	`conf`	Tree partition based parallel frequent pattern mining on shared memory systems. IPDPS Dehao Chen, Chunrong Lai, Wei Hu, Wenguang Chen, Yimin Zhang, Weimin Zheng