mlsanityData to 14 Oct 2026

QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core

arXiv 2111.09547 ↗First posted 18 Nov 2021cs.DCcited by 18

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Abstract (arXiv)

Over the most recent years, quantized graph neural network (QGNN) attracts lots of research and industry attention due to its high robustness and low computation and memory overhead. Unfortunately, the performance gains of QGNN have never been realized on modern GPU platforms. To this end, we propose the first Tensor Core (TC) based computing framework, QGTC, to support any-bitwidth computation for QGNNs on GPUs. We introduce a novel quantized low-bit arithmetic design based on the low-bit data representation and bit-decomposed computation. We craft a novel TC-tailored CUDA kernel design by incorporating 3D-stacked bit compression, zero-tile jumping, and non-zero tile reuse technique to improve the performance systematically. We incorporate an effective bandwidth-optimized subgraph packing strategy to maximize the transferring efficiency between CPU host and GPU device. We integrate QGTC with Pytorch for better programmability and extensibility. Extensive experiments demonstrate that QGTC achieves on average 2.7x speedup compared with the state-of-the-art Deep Graph Library framework across diverse settings.

Key facts. Cited by 18 papers in the map (data through 14 Oct 2026). Builds on 18 of its 42 references that are in the map. First posted on arXiv 18 Nov 2021.

Builds on

18 of 42 references.

References · 18

  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. 2017Graph Attention Networkscited by 8,923
  4. 2017Inductive Representation Learning on Large Graphscited by 6,320
  5. 2018How Powerful are Graph Neural Networks?cited by 4,393
  6. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  7. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  8. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
  9. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 787
  10. 2019Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networkscited by 541
  11. 2019Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networkscited by 242
  12. 2020Degree-Quant: Quantization-Aware Training for Graph Neural Networkscited by 96
6 more
  1. 2020Binary Graph Neural Networkscited by 34
  2. 2019GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platformscited by 31
  3. 2020SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantizationcited by 27
  4. 2021POLE: Polarized Embedding for Signed Networkscited by 17
  5. 2021A Broader Picture of Random-walk Based Graph Embeddingcited by 16
  6. 2020Accelerating Binarized Neural Networks via Bit-Tensor-Cores in Turing GPUscited by 14

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 5; 2023: 5; 2024: 3; 2025: 3; 2026: 1.

18 papers.

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  1. 2022SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learningcited by 47
  2. 2022Distributed Graph Neural Network Training: A Surveycited by 32
  3. 2022Global Counterfactual Explainer for Graph Neural Networkscited by 30
  4. 2021TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUscited by 17
  5. 2023The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Surveycited by 14
  6. 2023Low-bit Quantization for Deep Graph Neural Networks with Smoothness-aware Message Propagationcited by 5
  7. 2024DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Trainingcited by 5
  8. 2023Tango: rethinking quantization for graph neural network training on GPUscited by 4
  9. 2024Quasar-ViT: Hardware-Oriented Quantization-Aware Architecture Search for Vision Transformerscited by 4
  10. 2023Boosting Distributed Full-graph GNN Training with Asynchronous One-bit Communicationcited by 3
  11. 2025Efficient Mixed Precision Quantization in Graph Neural Networkscited by 3
  12. 2024FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUscited by 2
6 more
  1. 2025FlexQ: Efficient Post-training INT6 Quantization for LLM Serving via Algorithm-System Co-Designcited by 2
  2. 2023DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunkscited by 1
  3. 2025Libra: Unleashing GPU Heterogeneity for High-Performance Sparse Matrix Multiplicationcited by 1
  4. 2022Faith: An Efficient Framework for Transformer Verification on GPUscited by 0
  5. 2022AcceRL: Policy Acceleration Framework for Deep Reinforcement Learningcited by 0
  6. 2026Accelerating GPU Inference of Large Language Models with Moderately Unstructured Sparse Weight Matricescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2021APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores
  2. 2022BiFeat: Supercharge GNN Training via Graph Feature Quantization
  3. 2021LW-GCN: A Lightweight FPGA-based Graph Convolutional Network Accelerator
  4. 2023MEGA: A Memory-Efficient GNN Accelerator Exploiting Degree-Aware Mixed-Precision Quantization
  5. 2024Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores
  6. 2023Activation Compression of Graph Neural Networks using Block-wise Quantization with Improved Variance Minimization
  7. 2023GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis
  8. 2026NCCLZ: Compression-Enabled GPU Collectives with Decoupled Quantization and Entropy Coding