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...

Celý popis

Podrobná bibliografia

Vydané v:	Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design s. 01 - 09
Hlavní autori:	Xie, Xi, Peng, Hongwu, Hasan, Amit, Huang, Shaoyi, Zhao, Jiahui, Fang, Haowen, Zhang, Wei, Geng, Tong, Khan, Omer, Ding, Caiwen
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 28.10.2023
Predmet:	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
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!