ParamSpMM uses a Parameterized Compressed Sparse Row format and an ML-based decider to adapt SpMM optimizations for GNNs, delivering 1.92x average speedup over cuSPARSE.
Advances in neural information processing systems33, 22118–22133 (2020)
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A label-free Group Lasso method estimates important subgraphs in pretrained GNNs by incorporating domain structural knowledge.
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ParamSpMM: Adaptive and Efficient Sparse Matrix-Matrix Multiplication on GPUs for GNNs
ParamSpMM uses a Parameterized Compressed Sparse Row format and an ML-based decider to adapt SpMM optimizations for GNNs, delivering 1.92x average speedup over cuSPARSE.
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Estimating Subgraph Importance with Structural Prior Domain Knowledge
A label-free Group Lasso method estimates important subgraphs in pretrained GNNs by incorporating domain structural knowledge.