mlsanityData to 14 Oct 2026

AdaptGear: Accelerating GNN Training via Adaptive Subgraph-Level Kernels on GPUs

arXiv 2305.17408 ↗First posted 27 May 2023cs.DC cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

Graph neural networks (GNNs) are powerful tools for exploring and learning from graph structures and features. As such, achieving high-performance execution for GNNs becomes crucially important. Prior works have proposed to explore the sparsity (i.e., low density) in the input graph to accelerate GNNs, which uses the full-graph-level or block-level sparsity format. We show that they fail to balance the sparsity benefit and kernel execution efficiency. In this paper, we propose a novel system, referred to as AdaptGear, that addresses the challenge of optimizing GNNs performance by leveraging kernels tailored to the density characteristics at the subgraph level. Meanwhile, we also propose a method that dynamically chooses the optimal set of kernels for a given input graph. Our evaluation shows that AdaptGear can achieve a significant performance improvement, up to 6.49 × (1.87 × on average), over the state-of-the-art works on two mainstream NVIDIA GPUs across various datasets.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 34 of its 84 references that are in the map. First posted on arXiv 27 May 2023.

Builds on

34 of 84 references.

References · 34

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2018How Powerful are Graph Neural Networks?cited by 4,393
  5. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  6. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  7. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  8. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  9. 2019Graph Neural Networks for Social Recommendationcited by 714
  10. 2020Benchmarking Graph Neural Networkscited by 680
  11. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 494
  12. 2020Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged Fraudsterscited by 250
22 more
  1. 2020Machine Learning on Graphs: A Model and Comprehensive Taxonomycited by 156
  2. 2019AliGraph: A Comprehensive Graph Neural Network Platformcited by 153
  3. 2021Graph Neural Networks in Network Neurosciencecited by 92
  4. 2021UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendationcited by 87
  5. 2020Computing Graph Neural Networks: A Survey from Algorithms to Acceleratorscited by 81
  6. 2019Spam Review Detection with Graph Convolutional Networkscited by 57
  7. 2022SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximationcited by 55
  8. 2020GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUscited by 51
  9. 2022ANT: Exploiting Adaptive Numerical Data Type for Low-bit Deep Neural Network Quantizationcited by 51
  10. 2020GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networkscited by 42
  11. 2020FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systemscited by 36
  12. 2020Accelerating Sparse DNN Models without Hardware-Support via Tile-Wise Sparsitycited by 34
  13. 2021Dual-side Sparse Tensor Corecited by 29
  14. 2021Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspectivecited by 28
  15. 2015Gunrock: A High-Performance Graph Processing Library on the GPUcited by 14
  16. 2022VELTAIR: Towards High-Performance Multi-tenant Deep Learning Services via Adaptive Compilation and Schedulingcited by 14
  17. 2020Balancing Efficiency and Flexibility for DNN Acceleration via Temporal GPU-Systolic Array Integrationcited by 8
  18. 2021Characterizing and Demystifying the Implicit Convolution Algorithm on Commercial Matrix-Multiplication Acceleratorscited by 8
  19. 2015Dissecting GPU Memory Hierarchy through Microbenchmarkingcited by 6
  20. 2018GraphIt: A High-Performance DSL for Graph Analyticscited by 5
  21. 2019A Survey on Graph Processing Accelerators: Challenges and Opportunitiescited by 5
  22. 2022Nesting Forward Automatic Differentiation for Memory-Efficient Deep Neural Network Trainingcited by 2

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025AutoSAGE: Input-Aware CUDA Scheduling for Sparse GNN Aggregation (SpMM/SDDMM) and CSR Attentioncited by 0