MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training

1 code implementation • 14 Dec 2023 • Hongwu Peng, Xi Xie, Kaustubh Shivdikar, MD Amit Hasan, Jiahui Zhao, Shaoyi Huang, Omer Khan, David Kaeli, Caiwen Ding

In this paper, we present MaxK-GNN, an advanced high-performance GPU training system integrating algorithm and system innovation.

Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks

1 code implementation • 22 Aug 2023 • Xi Xie, Hongwu Peng, Amit Hasan, Shaoyi Huang, Jiahui Zhao, Haowen Fang, Wei zhang, Tong Geng, Omer Khan, Caiwen Ding

Utilizing these principles, we formulated a kernel for sparse matrix multiplication (SpMM) in GCNs that employs block-level partitioning and combined warp strategy.

Computational Efficiency

Towards Real-Time Temporal Graph Learning

1 code implementation • 8 Oct 2022 • Deniz Gurevin, Mohsin Shan, Tong Geng, Weiwen Jiang, Caiwen Ding, Omer Khan

Prior work operates on pre-collected temporal graph data and is not designed to handle updates on a graph in real-time.

graph construction Graph Learning +3

Towards Sparsification of Graph Neural Networks

1 code implementation • 11 Sep 2022 • Hongwu Peng, Deniz Gurevin, Shaoyi Huang, Tong Geng, Weiwen Jiang, Omer Khan, Caiwen Ding

In this paper, we utilize two state-of-the-art model compression methods (1) train and prune and (2) sparse training for the sparsification of weight layers in GNNs.

Image Classification Link Prediction +4