mlsanityData to 14 Oct 2026

AutoSAGE: Input-Aware CUDA Scheduling for Sparse GNN Aggregation (SpMM/SDDMM) and CSR Attention

arXiv 2511.17594 ↗First posted 17 Nov 2025cs.LG cs.PFcited by 0

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Abstract (arXiv)

Sparse GNN aggregations (CSR SpMM/SDDMM) vary widely in performance with degree skew, feature width, and GPU micro-architecture. We present AutoSAGE, an input-aware CUDA scheduler that chooses tiling and mapping per input using a lightweight estimate refined by on-device micro-probes, with a guardrail that safely falls back to vendor kernels and a persistent cache for deterministic replay. AutoSAGE covers SpMM and SDDMM and composes into a CSR attention pipeline (SDDMM -> row-softmax -> SpMM). On Reddit and OGBN-Products, it matches vendor baselines at bandwidth-bound feature widths and finds gains at small widths; on synthetic sparsity and skew stress tests it achieves up to 4.7x kernel-level speedups. We release CUDA sources, Python bindings, a reproducible harness, and replayable cache logs.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 24 references that are in the map. First posted on arXiv 17 Nov 2025.

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12 of 24 references.

References · 12

  1. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  2. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  3. 2020Ansor: Generating High-Performance Tensor Programs for Deep Learningcited by 201
  4. 2020Design Space for Graph Neural Networkscited by 182
  5. 2020Sparse GPU Kernels for Deep Learningcited by 141
  6. 2020GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUscited by 51
  7. 2021BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessingcited by 50
  8. 2020GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networkscited by 42
  9. 2020FusedMM: A Unified SDDMM-SpMM Kernel for Graph Embedding and Graph Neural Networkscited by 26
  10. 2021TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUscited by 17
  11. 2019GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPUcited by 6
  12. 2023AdaptGear: Accelerating GNN Training via Adaptive Subgraph-Level Kernels on GPUscited by 1

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