Reducing DRAM row activations with eager read/write clustering. - Myeongjae Jeon; Conglong Li; Alan L. Cox; Scott Rixner (ACM Trans. Archit. Code Optim., 2013)

Scheduling techniques for hybrid circuit/packet networks. - He Liu; Matthew K. Mukerjee; Conglong Li; Nicolas Feltman; George Papen; Stefan Savage; Srinivasan Seshan; Geoffrey M. Voelker; David G. Andersen; Michael Kaminsky; George Porter; Alex C. Snoeren (CoNEXT, 2015)

GD-Wheel: a cost-aware replacement policy for key-value stores. - Conglong Li; Alan L. Cox (EuroSys, 2015)

Using Indirect Routing to Recover from Network Traffic Scheduling Estimation Error. - Conglong Li; Matthew K. Mukerjee; David G. Andersen; Srinivasan Seshan; Michael Kaminsky; George Porter; Alex C. Snoeren (ANCS, 2017)

Workload analysis and caching strategies for search advertising systems. - Conglong Li; David G. Andersen; Qiang Fu; Sameh Elnikety; Yuxiong He (SoCC, 2017)

Better Caching in Search Advertising Systems with Rapid Refresh Predictions. - Conglong Li; David G. Andersen; Qiang Fu; Sameh Elnikety; Yuxiong He (WWW, 2018)

Scaling Video Analytics on Constrained Edge Nodes. - Christopher Canel; Thomas Kim; Giulio Zhou; Conglong Li; Hyeontaek Lim; David G. Andersen; Michael Kaminsky; Subramanya Dulloor (SysML, 2019)

Improving Approximate Nearest Neighbor Search through Learned Adaptive Early Termination. - Conglong Li; Minjia Zhang; David G. Andersen; Yuxiong He (SIGMOD Conference, 2020)

1-bit Adam: Communication Efficient Large-Scale Training with Adam's Convergence Speed. - Hanlin Tang; Shaoduo Gan; Ammar Ahmad Awan; Samyam Rajbhandari; Conglong Li; Xiangru Lian; Ji Liu 002; Ce Zhang 001; Yuxiong He (ICML, 2021)

Curriculum Learning: A Regularization Method for Efficient and Stable Billion-Scale GPT Model Pre-Training. - Conglong Li; Minjia Zhang; Yuxiong He (CoRR, 2021)

1-bit LAMB: Communication Efficient Large-Scale Large-Batch Training with LAMB's Convergence Speed. - Conglong Li; Ammar Ahmad Awan; Hanlin Tang; Samyam Rajbhandari; Yuxiong He (HIPC, 2022)

DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale. - Samyam Rajbhandari; Conglong Li; Zhewei Yao; Minjia Zhang; Reza Yazdani Aminabadi; Ammar Ahmad Awan; Jeff Rasley; Yuxiong He (ICML, 2022)

The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models. - Conglong Li; Minjia Zhang; Yuxiong He (NeurIPS, 2022)

XTC: Extreme Compression for Pre-trained Transformers Made Simple and Efficient. - Xiaoxia Wu; Zhewei Yao; Minjia Zhang; Conglong Li; Yuxiong He (NeurIPS, 2022)

ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers. - Zhewei Yao; Reza Yazdani Aminabadi; Minjia Zhang; Xiaoxia Wu; Conglong Li; Yuxiong He (NeurIPS, 2022)

Extreme Compression for Pre-trained Transformers Made Simple and Efficient. - Xiaoxia Wu; Zhewei Yao; Minjia Zhang; Conglong Li; Yuxiong He (CoRR, 2022)

Random-LTD: Random and Layerwise Token Dropping Brings Efficient Training for Large-scale Transformers. - Zhewei Yao; Xiaoxia Wu; Conglong Li; Connor Holmes; Minjia Zhang; Cheng Li 001; Yuxiong He (CoRR, 2022)

Maximizing Communication Efficiency for Large-scale Training via 0/1 Adam. - Yucheng Lu; Conglong Li; Minjia Zhang; Christopher De Sa; Yuxiong He (ICLR, 2023)

DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales. - Zhewei Yao; Reza Yazdani Aminabadi; Olatunji Ruwase; Samyam Rajbhandari; Xiaoxia Wu; Ammar Ahmad Awan; Jeff Rasley; Minjia Zhang; Conglong Li; Connor Holmes; Zhongzhu Zhou; Michael Wyatt; et al. (CoRR, 2023)

DeepSpeed-VisualChat: Multi-Round Multi-Image Interleave Chat via Multi-Modal Causal Attention. - Zhewei Yao; Xiaoxia Wu; Conglong Li; Minjia Zhang; Heyang Qin; Olatunji Ruwase; Ammar Ahmad Awan; Samyam Rajbhandari; Yuxiong He (CoRR, 2023)

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies. - Shuaiwen Leon Song; Bonnie Kruft; Minjia Zhang; Conglong Li; Shiyang Chen 004; Chengming Zhang 006; Masahiro Tanaka; Xiaoxia Wu; Jeff Rasley; Ammar Ahmad Awan; Connor Holmes; Martin Cai; et al. (CoRR, 2023)

DeepSpeed Data Efficiency: Improving Deep Learning Model Quality and Training Efficiency via Efficient Data Sampling and Routing. - Conglong Li; Zhewei Yao; Xiaoxia Wu; Minjia Zhang; Connor Holmes; Cheng Li 001; Yuxiong He (AAAI, 2024)

LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs. - Akiko Aizawa; Eiji Aramaki; Bowen Chen; Fei Cheng 002; Hiroyuki Deguchi 002; Rintaro Enomoto; Kazuki Fujii; Kensuke Fukumoto; Takuya Fukushima; Namgi Han; Yuto Harada; Chikara Hashimoto; et al. (CoRR, 2024)