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GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks [article]

Guyue Huang, Guohao Dai, Yu Wang, Huazhong Yang
2020 arXiv   pre-print
Graph Neural Networks (GNNs) have achieved significant improvements in various domains.  ...  Sparse Matrix-Matrix multiplication (SpMM) is a fundamental operator in GNNs, which performs a multiplication between a sparse matrix and a dense matrix.  ...  CONCLUSION In this paper, we propose an efficient CSR-based SpMM design, GE-SpMM, for Graph Neural Network applications on GPUs.  ... 
arXiv:2007.03179v1 fatcat:epshcpa7fbbdbcjchdkrocej3m

Computing Graph Neural Networks: A Survey from Algorithms to Accelerators [article]

Sergi Abadal, Akshay Jain, Robert Guirado, Jorge López-Alonso, Eduard Alarcón
2021 arXiv   pre-print
Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to their capability to model and learn from graph-structured data.  ...  Such an ability has strong implications in a wide variety of fields whose data is inherently relational, for which conventional neural networks do not perform well.  ...  ACKNOWLEDGMENTS The authors would like to thank the anonymous reviewers and the editorial team for their constructive criticism, which has helped improve the quality of the paper.  ... 
arXiv:2010.00130v3 fatcat:u5bcmjodcfdh7pew4nssjemdba

FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networks [article]

Md. Khaledur Rahman, Majedul Haque Sujon, Ariful Azad
2021 arXiv   pre-print
We develop a fused matrix multiplication kernel that unifies sampled dense-dense matrix multiplication and sparse-dense matrix multiplication under a single operation called FusedMM.  ...  FusedMM can tune its performance using a code generator and perform equally well on Intel, AMD and ARM processors.  ...  [17] developed a general-purpose SpMM algorithm for GPUs.  ... 
arXiv:2011.06391v2 fatcat:3qh7paknwfbn7mb7vg6z3bltia