Zhongming Yu

San Diego, California

I am a second-year Ph.D. advised by Prof. Jishen Zhao in Computer Science and Engineering Department of UC San Diego, starting in 2022. I am now aiming to build efficient systems for machine learning. Before coming to UCSD, I received my B.E. degree from the Department of Electronic Engineering at Tsinghua University. I have a broad interest in combining machine learning and computer systems, with a special focus on designing hardware-efficient algorithms. I have previously focused on more acceleration for GNN workloads and devoted myself to some open-source projects like dgSPARSE and CogDL. Some topics that I have been working on are listed below:

High-performance GPU Kernels(e.g. SpMM, SDDMM, Fused-Attention)
End-to-end optimization for sparse ML models including Submanifold Sparse Convolutional Networks, Graph Neural Networks, etc.
Machine Learning Compiler
In/Near-Storage Computing

Feel free to contact me if you share any common interests.

news

Apr 3, 2023	I am glad to serve as a reviewer in KDD’2023
Apr 15, 2022	I am honored to be an intern at NVIDIA this summer, working on GNN acceleration

selected publications

mlsys

Understanding gnn computational graph: A coordinated computation, io, and memory perspective

Hengrui Zhang*, Zhongming Yu*, Guohao Dai, and 4 more authors

Proceedings of Machine Learning and Systems, 2022

* stands for equal contribution

Bib HTML Code

@article{zhang2022understanding,
  title = {Understanding gnn computational graph: A coordinated computation, io, and memory perspective},
  author = {Zhang*, Hengrui and Yu*, Zhongming and Dai, Guohao and Huang, Guyue and Ding, Yufei and Xie, Yuan and Wang, Yu},
  journal = {Proceedings of Machine Learning and Systems},
  note = {* stands for equal contribution},
  volume = {4},
  pages = {467--484},
  year = {2022},
}

mlsys

HyperGef: A Framework Enabling Efficient Fusion for Hypergraph Neural Network on GPUs

Zhongming Yu, Guohao Dai, Shang Yang, and 6 more authors

Proceedings of Machine Learning and Systems, 2023

Bib HTML Code

@article{yu2023hypergef,
  title = {HyperGef: A Framework Enabling Efficient Fusion for Hypergraph Neural Network on GPUs},
  author = {Yu, Zhongming and Dai, Guohao and Yang, Shang and Zhang, Genghan and Zhang, Hengrui and Zhu, Feiwen and Yang, June and Zhao, Jishen and Wang, Yu},
  journal = {Proceedings of Machine Learning and Systems},
  volume = {5},
  year = {2023},
}

mlsys

Exploiting Hardware Utilization and Adaptive Dataflow for Efficient Sparse Convolution in 3D Point Clouds

Ke Hong*, Zhongming Yu*, Guohao Dai, and 4 more authors

Proceedings of Machine Learning and Systems, 2023

* stands for equal contribution

Bib HTML Code

@article{hong2023exploiting,
  title = {Exploiting Hardware Utilization and Adaptive Dataflow for Efficient Sparse Convolution in 3D Point Clouds},
  author = {Hong*, Ke and Yu*, Zhongming and Dai, Guohao and Yang, Xinhao and Lian, Yaoxiu and Xu, Ningyi and Wang, Yu},
  journal = {Proceedings of Machine Learning and Systems},
  volume = {5},
  note = {* stands for equal contribution},
  year = {2023},
}