iSpLib: A Library for Accelerating Graph Neural Networks using Auto-tuned Sparse Operations

arXiv:2403.14853v1 Announce Type: new
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