2023 www Cogdl: A comprehensive library for graph deep learning Yukuo Cen, Zhenyu Hou, Yan Wang, and 8 more authors In Proceedings of the ACM Web Conference 2023, 2023 Bib HTML Code @inproceedings{cen2023cogdl, title = {Cogdl: A comprehensive library for graph deep learning}, author = {Cen, Yukuo and Hou, Zhenyu and Wang, Yan and Chen, Qibin and Luo, Yizhen and Yu, Zhongming and Zhang, Hengrui and Yao, Xingcheng and Zeng, Aohan and Guo, Shiguang and others}, booktitle = {Proceedings of the ACM Web Conference 2023}, pages = {747--758}, year = {2023}, } 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}, } cvpr TorchSparse++: Efficient Point Cloud Engine Haotian Tang, Shang Yang, Zhijian Liu, and 6 more authors In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023 Poster Bib HTML Code @inproceedings{tang2023torchsparse++, title = {TorchSparse++: Efficient Point Cloud Engine}, author = {Tang, Haotian and Yang, Shang and Liu, Zhijian and Hong, Ke and Yu, Zhongming and Li, Xiuyu and Dai, Guohao and Wang, Yu and Han, Song}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, pages = {202--209}, note = {Poster}, 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}, } 2022 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}, } arxiv Benchmarking GNN-Based Recommender Systems on Intel Optane Persistent Memory Yuwei Hu, Jiajie Li, Zhongming Yu, and 1 more author arXiv preprint arXiv:2207.11918, 2022 Bib HTML @article{hu2022benchmarking, title = {Benchmarking GNN-Based Recommender Systems on Intel Optane Persistent Memory}, author = {Hu, Yuwei and Li, Jiajie and Yu, Zhongming and Zhang, Zhiru}, journal = {arXiv preprint arXiv:2207.11918}, year = {2022}, } 2021 iccd Exploiting Online Locality and Reduction Parallelism for Sampled Dense Matrix Multiplication on GPUs Zhongming Yu, Guohao Dai, Guyue Huang, and 2 more authors In 2021 IEEE 39th International Conference on Computer Design (ICCD), 2021 Bib HTML Code @inproceedings{yu2021exploiting, title = {Exploiting Online Locality and Reduction Parallelism for Sampled Dense Matrix Multiplication on GPUs}, author = {Yu, Zhongming and Dai, Guohao and Huang, Guyue and Wang, Yu and Yang, Huazhong}, booktitle = {2021 IEEE 39th International Conference on Computer Design (ICCD)}, pages = {567--574}, year = {2021}, organization = {IEEE} }