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

Graph Convolutional Networks (GCNs) are pivotal in extracting latent information from graph data across various domains, yet their acceleration on mainstream GPUs is challenged by workload imbalance and memory access irregularity. To address these challenges, we present Accel-GCN, a GPU accelerator...

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Bibliographic Details

Published in:	Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design pp. 01 - 09
Main Authors:	Xie, Xi, Peng, Hongwu, Hasan, Amit, Huang, Shaoyi, Zhao, Jiahui, Fang, Haowen, Zhang, Wei, Geng, Tong, Khan, Omer, Ding, Caiwen
Format:	Conference Proceeding
Language:	English
Published:	IEEE 28.10.2023
Subjects:	Accelerator architectures Bandwidth Benchmark testing Convolution GPUs Graph Convolution Network Graphics processing units Memory management parallel computing Parallel processing sparse matrix multiplication (SpMM)
ISSN:	1558-2434
Online Access:	Get full text
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