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iSpLib: A Library for Accelerating Graph Neural Networks using Auto-tuned Sparse Operations
March 25, 2024, 4:41 a.m. | Md Saidul Hoque Anik, Pranav Badhe, Rohit Gampa, Ariful Azad
cs.LG updates on arXiv.org arxiv.org
Abstract: Core computations in Graph Neural Network (GNN) training and inference are often mapped to sparse matrix operations such as sparse-dense matrix multiplication (SpMM). These sparse operations are harder to optimize by manual tuning because their performance depends significantly on the sparsity of input graphs, GNN models, and computing platforms. To address this challenge, we present iSpLib, a PyTorch-based C++ library equipped with auto-tuned sparse operations. iSpLib expedites GNN training with a cache-enabled backpropagation that stores …
arxiv auto cs.dc cs.lg cs.pf graph graph neural networks library networks neural networks operations type
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