Scalable De Novo Genome Assembly Using Pregel. - Da Yan 001; Hongzhi Chen; James Cheng; Zhenkun Cai; Bin Shao (ICDE, 2018)

FlexPS: Flexible Parallelism Control in Parameter Server Architecture. - Yuzhen Huang; Tatiana Jin; Yidi Wu; Zhenkun Cai; Xiao Yan 002; Fan Yang 091; Jinfeng Li; Yuying Guo; James Cheng (Proc. VLDB Endow., 2018)

Improving resource utilization by timely fine-grained scheduling. - Tatiana Jin; Zhenkun Cai; Boyang Li 016; Chengguang Zheng; Guanxian Jiang; James Cheng (EuroSys, 2020)

DGCL: an efficient communication library for distributed GNN training. - Zhenkun Cai; Xiao Yan 002; Yidi Wu; Kaihao Ma; James Cheng; Fan Yu (EuroSys, 2021)

Seastar: vertex-centric programming for graph neural networks. - Yidi Wu; Kaihao Ma; Zhenkun Cai; Tatiana Jin; Boyang Li 016; Chengguang Zheng; James Cheng; Fan Yu (EuroSys, 2021)

DGI: Easy and Efficient Inference for GNNs. - Peiqi Yin; Xiao Yan 002; Jinjing Zhou; Qiang Fu; Zhenkun Cai; James Cheng; Bo Tang 016; Minjie Wang (CoRR, 2022)

TensorOpt: Exploring the Tradeoffs in Distributed DNN Training With Auto-Parallelism. - Zhenkun Cai; Xiao Yan 002; Kaihao Ma; Yidi Wu; Yuzhen Huang; James Cheng; Teng Su; Fan Yu (IEEE Trans. Parallel Distributed Syst., 2022)

Elastic Deep Learning in Multi-Tenant GPU Clusters. - Yidi Wu; Kaihao Ma; Xiao Yan 002; Zhi Liu; Zhenkun Cai; Yuzhen Huang; James Cheng; Han Yuan; Fan Yu (IEEE Trans. Parallel Distributed Syst., 2022)

DGI: An Easy and Efficient Framework for GNN Model Evaluation. - Peiqi Yin; Xiao Yan 002; Jinjing Zhou; Qiang Fu; Zhenkun Cai; James Cheng; Bo Tang 016; Minjie Wang (KDD, 2023)

DSP: Efficient GNN Training with Multiple GPUs. - Zhenkun Cai; Qihui Zhou; Xiao Yan 002; Da Zheng; Xiang Song 003; Chenguang Zheng; James Cheng; George Karypis (PPoPP, 2023)

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)

MuseGNN: Interpretable and Convergent Graph Neural Network Layers at Scale. - Haitian Jiang; Renjie Liu 001; Xiao Yan 002; Zhenkun Cai; Minjie Wang; David Wipf (CoRR, 2023)

FEC: Efficient Deep Recommendation Model Training with Flexible Embedding Communication. - Kaihao Ma; Xiao Yan 002; Zhenkun Cai; Yuzhen Huang; Yidi Wu; James Cheng (Proc. ACM Manag. Data, 2023)

DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs. - Jiahui Liu; Zhenkun Cai; Zhiyong Chen; Minjie Wang (LoG, 2024)

4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on RDBs. - Minjie Wang; Quan Gan; David Wipf; Zheng Zhang 001; Christos Faloutsos; Weinan Zhang 001; Muhan Zhang; Zhenkun Cai; Jiahang Li 002; Zunyao Mao; Yakun Song; Jianheng Tang; et al. (NeurIPS, 2024)

4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on Relational DBs. - Minjie Wang; Quan Gan; David Wipf; Zhenkun Cai; Ning Li 029; Jianheng Tang; Yanlin Zhang; Zizhao Zhang; Zunyao Mao; Yakun Song; Yanbo Wang; Jiahang Li 002; et al. (CoRR, 2024)

PPS: Fair and efficient black-box scheduling for multi-tenant GPU clusters. - Kaihao Ma; Zhenkun Cai; Xiao Yan 002; Yang Zhang; Zhi Liu; Yihui Feng; Chao Li; Wei Lin; James Cheng (Parallel Comput., 2024)

MuseGNN: Forming Scalable, Convergent GNN Layers that Minimize a Sampling-Based Energy. - Haitian Jiang; Renjie Liu 001; Zengfeng Huang; Yichuan Wang 002; Xiao Yan 002; Zhenkun Cai; Minjie Wang; David Wipf (ICLR, 2025)

Adaptive Parallel Training for Graph Neural Networks. - Kaihao Ma; Renjie Liu 001; Xiao Yan 002; Zhenkun Cai; Xiang Song 003; Minjie Wang; Yichao Li; James Cheng (PPoPP, 2025)

DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training. - Renjie Liu 001; Yichuan Wang 002; Xiao Yan 002; Haitian Jiang; Zhenkun Cai; Minjie Wang; Bo Tang 016; Jinyang Li 001 (Proc. ACM Manag. Data, 2025)