Search Results for author: Zhongming Yu

Found 6 papers, 4 papers with code

GeoT: Tensor Centric Library for Graph Neural Network via Efficient Segment Reduction on GPU

1 code implementation3 Apr 2024 Zhongming Yu, Genghan Zhang, Hanxian Huang, Xin Chen, Jishen Zhao

Yet, efficient tensor-centric frameworks for GNNs remain scarce due to unique challenges and limitations encountered when implementing segment reduction in GNN contexts.

Graph Neural Network

TorchSparse++: Efficient Training and Inference Framework for Sparse Convolution on GPUs

1 code implementation25 Oct 2023 Haotian Tang, Shang Yang, Zhijian Liu, Ke Hong, Zhongming Yu, Xiuyu Li, Guohao Dai, Yu Wang, Song Han

On top of this, we design the Sparse Autotuner, which extends the design space of existing sparse convolution libraries and searches for the best dataflow configurations for training and inference workloads.

Autonomous Driving Recommendation Systems

Analysis and Optimization of GNN-Based Recommender Systems on Persistent Memory

no code implementations25 Jul 2022 Yuwei Hu, Jiajie Li, Zhongming Yu, Zhiru Zhang

To understand whether persistent memory is a good fit for GNNRecSys training, we perform an in-depth characterization of GNNRecSys workloads and a comprehensive analysis of their performance on a persistent memory device, namely, Intel Optane.

Link Prediction Recommendation Systems

Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective

no code implementations18 Oct 2021 Hengrui Zhang, Zhongming Yu, Guohao Dai, Guyue Huang, Yufei Ding, Yuan Xie, Yu Wang

The same data are propagated through the graph structure to perform the same neural operation multiple times in GNNs, leading to redundant computation which accounts for 92. 4% of total operators.

CogDL: A Comprehensive Library for Graph Deep Learning

1 code implementation1 Mar 2021 Yukuo Cen, Zhenyu Hou, Yan Wang, Qibin Chen, Yizhen Luo, Zhongming Yu, Hengrui Zhang, Xingcheng Yao, Aohan Zeng, Shiguang Guo, Yuxiao Dong, Yang Yang, Peng Zhang, Guohao Dai, Yu Wang, Chang Zhou, Hongxia Yang, Jie Tang

In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries.

Graph Classification Graph Embedding +5

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