# GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs

arXiv: https://arxiv.org/abs/2006.06608 · HTML: https://mlsanity.com/paper/2006.06608/

## Key facts

- Cited by 51 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2020.

## Built on by (most cited first, 30 of 51)

- [BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessing](https://mlsanity.com/paper/2112.08541/) (2021)
- [SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning](https://mlsanity.com/paper/2207.04606/) (2022)
- [Accelerating Training and Inference of Graph Neural Networks with Fast Sampling and Pipelining](https://mlsanity.com/paper/2110.08450/) (2021)
- [PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm](https://mlsanity.com/paper/2203.00638/) (2022)
- [Distributed Graph Neural Network Training: A Survey](https://mlsanity.com/paper/2211.00216/) (2022)
- [Understanding GNN Computational Graph: A Coordinated Computation, IO, and Memory Perspective](https://mlsanity.com/paper/2110.09524/) (2021)
- [MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networks](https://mlsanity.com/paper/2202.02365/) (2022)
- [A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware](https://mlsanity.com/paper/2306.14052/) (2023)
- [Communication-Efficient Graph Neural Networks with Probabilistic Neighborhood Expansion Analysis and Caching](https://mlsanity.com/paper/2305.03152/) (2023)
- [The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey](https://mlsanity.com/paper/2305.13854/) (2023)
- [SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures](https://mlsanity.com/paper/2205.04711/) (2022)
- [GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networks](https://mlsanity.com/paper/2203.00158/) (2022)
- [DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training](https://mlsanity.com/paper/2405.05231/) (2024)
- [TorchGT: A Holistic System for Large-scale Graph Transformer Training](https://mlsanity.com/paper/2407.14106/) (2024)
- [PiPAD: Pipelined and Parallel Dynamic GNN Training on GPUs](https://mlsanity.com/paper/2301.00391/) (2023)
- [FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Training](https://mlsanity.com/paper/2301.07482/) (2023)
- [GNNIE: GNN Inference Engine with Load-balancing and Graph-Specific Caching](https://mlsanity.com/paper/2105.10554/) (2021)
- [Distributed Graph Neural Network Training with Periodic Stale Representation Synchronization](https://mlsanity.com/paper/2206.00057/) (2022)
- [Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry](https://mlsanity.com/paper/2211.13853/) (2022)
- [HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning](https://mlsanity.com/paper/2403.05763/) (2024)
- [GNNBENCH: Fair and Productive Benchmarking for Single-GPU GNN System](https://mlsanity.com/paper/2404.04118/) (2024)
- [FastGL: A GPU-Efficient Framework for Accelerating Sampling-Based GNN Training at Large Scale](https://mlsanity.com/paper/2409.14939/) (2024)
- [FlashSparse: Minimizing Computation Redundancy for Fast Sparse Matrix Multiplications on Tensor Cores](https://mlsanity.com/paper/2412.11007/) (2024)
- [DGI: Easy and Efficient Inference for GNNs](https://mlsanity.com/paper/2211.15082/) (2022)
- [Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services](https://mlsanity.com/paper/2307.01684/) (2023)
- [Exploiting On-chip Heterogeneity of Versal Architecture for GNN Inference Acceleration](https://mlsanity.com/paper/2308.02749/) (2023)
- [GNNavigator: Towards Adaptive Training of Graph Neural Networks via Automatic Guideline Exploration](https://mlsanity.com/paper/2404.09544/) (2024)
- [Reforge: Low-Latency Distributed GNN Serving with Selective Embedding Recomputation](https://mlsanity.com/paper/2501.08547/) (2025)
- [Structural Alignment Improves Graph Test-Time Adaptation](https://mlsanity.com/paper/2502.18334/) (2025)
- [Efficient GNN Training Through Structure-Aware Randomized Mini-Batching](https://mlsanity.com/paper/2504.18082/) (2025)

## Abstract (arXiv)

As the emerging trend of graph-based deep learning, Graph Neural Networks (GNNs) excel for their capability to generate high-quality node feature vectors (embeddings). However, the existing one-size-fits-all GNN implementations are insufficient to catch up with the evolving GNN architectures, the ever-increasing graph sizes, and the diverse node embedding dimensionalities. To this end, we propose GNNAdvisor, an adaptive and efficient runtime system to accelerate various GNN workloads on GPU platforms. First, GNNAdvisor explores and identifies several performance-relevant features from both the GNN model and the input graph, and uses them as a new driving force for GNN acceleration. Second, GNNAdvisor implements a novel and highly-efficient 2D workload management, tailored for GNN computation to improve GPU utilization and performance under different application settings. Third, GNNAdvisor capitalizes on the GPU memory hierarchy for acceleration by gracefully coordinating the execution of GNNs according to the characteristics of the GPU memory structure and GNN workloads. Furthermore, to enable automatic runtime optimization, GNNAdvisor incorporates a lightweight analytical model for an effective design parameter search. Extensive experiments show that GNNAdvisor outperforms the state-of-the-art GNN computing frameworks, such as Deep Graph Library (3.02× faster on average) and NeuGraph (up to 4.10× faster), on mainstream GNN architectures across various datasets.