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

• URL: http://arxiv.org/abs/2511.17594v1
• Date: Mon, 17 Nov 2025 18:25:51 GMT
• Title: AutoSAGE: Input-Aware CUDA Scheduling for Sparse GNN Aggregation (SpMM/SDDMM) and CSR Attention
• Authors: Aleksandar Stankovic,
• Abstract summary: AutoSAGE is an input-aware scheduler that chooses tiling and mapping per input.<n>On Reddit OGBN-Products it achieves up to 4.7x kernel-level speedups.
• Score: 52.20940151628735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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