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1 Ergebnisse
1
MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating..:
, In:
Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2
,
Peng, Hongwu
;
Xie, Xi
;
Shivdikar, Kaustubh
... - p. 683-698 , 2024
Link:
https://dl.acm.org/doi/10.1145/3620665.3640426
RT T1 <cTitle>Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2</cTitle>: T1 <Title>MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3640426&Exemplar=1&LAN=DE A1 Peng, Hongwu A1 Xie, Xi A1 Shivdikar, Kaustubh A1 Hasan, Md Amit A1 Zhao, Jiahui A1 Huang, Shaoyi A1 Khan, Omer A1 Kaeli, David A1 Ding, Caiwen PB ACM YR 2024 K1 graph neural network K1 SpMM K1 SpGEMM K1 sampled sparse matrix dense matrix multiplication (SSpMM) K1 MaxK nonlinearity K1 parallel computing K1 GPUs K1 Computer systems organization K1 Architectures K1 Computing methodologies K1 Parallel computing methodologies K1 Machine learning SP 683 OP 698 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3620665.3640426 DO https://dl.acm.org/doi/10.1145/3620665.3640426 SF ELIB - SuUB Bremen
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