KACB 3206

226 Ferst Drive NW

Atlanta, GA 30332

Cheng Wan (万诚)

Ph.D. Student at EIC Lab, Georgia Tech

I am a Ph.D. student at the School of Computer Science, Georgia Institute of Technology, advised by Prof. Yingyan (Celine) Lin from the EIC Lab. Before coming to Georgia Tech, I obtained my bachelor’s degree from ACM Honors Class at Shanghai Jiao Tong University in 2018, and received my master’s degree from Rice University in 2020, advised by Prof. Akane Sano. I have broad interests in algorithm-system co-design for machine learning systems, with a special focus on distributed training.

Selected Publications

  1. A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware
    arXiv preprint arXiv:2306.14052, 2023
  2. BNS-GCN: Efficient Full-graph Training of Graph Convolutional Networks with Partition-parallelism and Random Boundary Node Sampling
    Cheng Wan*Youjie Li*Ang LiNam Sung Kim, and Yingyan Lin
    In Proceedings of Machine Learning and Systems (MLSys), 2022
  3. PipeGCN: Efficient Full-graph Training of Graph Convolutional Networks with Pipelined Feature Communication
    In Proceedings of the International Conference on Learning Representations (ICLR), 2022


Reviewer ICLR 2023, LoG 2023, NeurIPS 2023, LoG 2022, ICML 2022
Artifact Evaluation Committee MLSys 2023, ASPLOS 2023, MICRO 2022
External Program Committee MLSys 2023