# QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core

arXiv: https://arxiv.org/abs/2111.09547 · HTML: https://mlsanity.com/paper/2111.09547/

## 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 (most cited first, 18 of 18)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks](https://mlsanity.com/paper/1905.07953/) (2019)
- [Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks](https://mlsanity.com/paper/1909.01315/) (2019)
- [Degree-Quant: Quantization-Aware Training for Graph Neural Networks](https://mlsanity.com/paper/2008.05000/) (2020)
- [Binary Graph Neural Networks](https://mlsanity.com/paper/2012.15823/) (2020)
- [GraphACT: Accelerating GCN Training on CPU-FPGA Heterogeneous Platforms](https://mlsanity.com/paper/2001.02498/) (2019)
- [SGQuant: Squeezing the Last Bit on Graph Neural Networks with Specialized Quantization](https://mlsanity.com/paper/2007.05100/) (2020)
- [POLE: Polarized Embedding for Signed Networks](https://mlsanity.com/paper/2110.09899/) (2021)
- [A Broader Picture of Random-walk Based Graph Embedding](https://mlsanity.com/paper/2110.12344/) (2021)
- [Accelerating Binarized Neural Networks via Bit-Tensor-Cores in Turing GPUs](https://mlsanity.com/paper/2006.16578/) (2020)

## Built on by (most cited first, 18 of 18)

- [SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning](https://mlsanity.com/paper/2207.04606/) (2022)
- [Distributed Graph Neural Network Training: A Survey](https://mlsanity.com/paper/2211.00216/) (2022)
- [Global Counterfactual Explainer for Graph Neural Networks](https://mlsanity.com/paper/2210.11695/) (2022)
- [TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs](https://mlsanity.com/paper/2112.02052/) (2021)
- [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)
- [Low-bit Quantization for Deep Graph Neural Networks with Smoothness-aware Message Propagation](https://mlsanity.com/paper/2308.14949/) (2023)
- [DiskGNN: Bridging I/O Efficiency and Model Accuracy for Out-of-Core GNN Training](https://mlsanity.com/paper/2405.05231/) (2024)
- [Tango: rethinking quantization for graph neural network training on GPUs](https://mlsanity.com/paper/2308.00890/) (2023)
- [Quasar-ViT: Hardware-Oriented Quantization-Aware Architecture Search for Vision Transformers](https://mlsanity.com/paper/2407.18175/) (2024)
- [Boosting Distributed Full-graph GNN Training with Asynchronous One-bit Communication](https://mlsanity.com/paper/2303.01277/) (2023)
- [Efficient Mixed Precision Quantization in Graph Neural Networks](https://mlsanity.com/paper/2505.09361/) (2025)
- [FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs](https://mlsanity.com/paper/2412.20796/) (2024)
- [FlexQ: Efficient Post-training INT6 Quantization for LLM Serving via Algorithm-System Co-Design](https://mlsanity.com/paper/2508.04405/) (2025)
- [DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks](https://mlsanity.com/paper/2309.03523/) (2023)
- [Libra: Unleashing GPU Heterogeneity for High-Performance Sparse Matrix Multiplication](https://mlsanity.com/paper/2506.22714/) (2025)
- [Faith: An Efficient Framework for Transformer Verification on GPUs](https://mlsanity.com/paper/2209.12708/) (2022)
- [AcceRL: Policy Acceleration Framework for Deep Reinforcement Learning](https://mlsanity.com/paper/2211.15023/) (2022)
- [Accelerating GPU Inference of Large Language Models with Moderately Unstructured Sparse Weight Matrices](https://mlsanity.com/paper/2607.08786/) (2026)

## 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.