Gradient Compression Supercharged High-Performance Data Parallel DNN Training. - Youhui Bai; Cheng Li 001; Quan Zhou; Jun Yi; Ping Gong 009; Feng Yan 001; Ruichuan Chen; Yinlong Xu (SOSP, 2021)

BiFeat: Supercharge GNN Training via Graph Feature Quantization. - Yuxin Ma; Ping Gong 009; Jun Yi; Zhewei Yao; Minjie Wang; Cheng Li 001; Yuxiong He; Feng Yan 001 (CoRR, 2022)

gSampler: General and Efficient GPU-based Graph Sampling for Graph Learning. - Ping Gong 009; Renjie Liu 001; Zunyao Mao; Zhenkun Cai; Xiao Yan 002; Cheng Li 001; Minjie Wang; Zhuozhao Li (SOSP, 2023)

Understand Data Preprocessing for Effective End-to-End Training of Deep Neural Networks. - Ping Gong 009; Yuxin Ma; Cheng Li 001; Xiaosong Ma; Sam H. Noh (CoRR, 2023)

Eliminating Data Processing Bottlenecks in GNN Training over Large Graphs via Two-level Feature Compression. - Yuxin Ma; Ping Gong 009; Tianming Wu; Jiawei Yi; Chengru Yang; Cheng Li 001; Qirong Peng; Guiming Xie; Yongcheng Bao; Haifeng Liu; Yinlong Xu (Proc. VLDB Endow., 2024)

HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference. - Ping Gong 009; Jiawei Yi; Shengnan Wang; Juncheng Zhang; Zewen Jin; Ouxiang Zhou; Ruibo Liu; Guanbin Xu; Youhui Bai; Bowen Ye; Kun Yuan; Tong Yang 003; et al. (ACL (Findings), 2025)

Efficient Long-Context LLM Inference via KV Cache Clustering. - Jie Hu; Shengnan Wang; Yutong He; Ping Gong 009; Jiawei Yi; Juncheng Zhang; Youhui Bai; Renhai Chen; Gong Zhang 001; Cheng Li 001; Kun Yuan (CoRR, 2025)