Design Principles for Sparse Matrix Multiplication on the GPU

22 March 2018

Papers citing "Design Principles for Sparse Matrix Multiplication on the GPU"

11 / 11 papers shown

Title
RDMA-Based Algorithms for Sparse Matrix Multiplication on GPUs Benjamin Brock A. Buluç Katherine Yelick 18 2 0 29 Nov 2023
Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks Xiaoru Xie Hongwu Peng Amit Hasan Shaoyi Huang Jiahui Zhao Haowen Fang Wei Zhang Tong Geng O. Khan Caiwen Ding GNN 30 30 0 22 Aug 2023
A Programming Model for GPU Load Balancing Muhammad Osama Serban D. Porumbescu John Douglas Owens 14 7 0 12 Jan 2023
Sgap: Towards Efficient Sparse Tensor Algebra Compilation for GPU Genghan Zhang Yuetong Zhao Yanting Tao Zhongming Yu Guohao Dai Sitao Huang Yuanyuan Wen Pavlos Petoumenos Yu Wang 41 4 0 07 Sep 2022
Do We Need Anisotropic Graph Neural Networks? Shyam A. Tailor Felix L. Opolka Pietro Lio' Nicholas D. Lane 40 34 0 03 Apr 2021
GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks Guyue Huang Guohao Dai Yu Wang Huazhong Yang GNN 24 121 0 07 Jul 2020
Sparse GPU Kernels for Deep Learning Trevor Gale Matei A. Zaharia C. Young Erich Elsen 17 227 0 18 Jun 2020
GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs Yuke Wang Boyuan Feng Gushu Li Shuangchen Li Lei Deng Yuan Xie Yufei Ding GNN 13 121 0 11 Jun 2020
Reducing Communication in Graph Neural Network Training Alok Tripathy Katherine Yelick A. Buluç GNN 22 104 0 07 May 2020
Fast Sparse ConvNets Erich Elsen Marat Dukhan Trevor Gale Karen Simonyan 21 151 0 21 Nov 2019
GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU Carl Yang A. Buluç John Douglas Owens GNN 16 97 0 04 Aug 2019