# FeatGraph: A Flexible and Efficient Backend for Graph Neural Network Systems

arXiv: https://arxiv.org/abs/2008.11359 · HTML: https://mlsanity.com/paper/2008.11359/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2020.

## Built on by (most cited first, 30 of 36)

- [TorchSparse: Efficient Point Cloud Inference Engine](https://mlsanity.com/paper/2204.10319/) (2022)
- [DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networks](https://mlsanity.com/paper/2104.06700/) (2021)
- [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)
- [Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/2205.09702/) (2022)
- [Motif Prediction with Graph Neural Networks](https://mlsanity.com/paper/2106.00761/) (2021)
- [A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware](https://mlsanity.com/paper/2306.14052/) (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)
- [Neural Graph Databases](https://mlsanity.com/paper/2209.09732/) (2022)
- [GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers](https://mlsanity.com/paper/2210.17281/) (2022)
- [HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers](https://mlsanity.com/paper/2311.18526/) (2023)
- [Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses](https://mlsanity.com/paper/2306.16384/) (2023)
- [Tango: rethinking quantization for graph neural network training on GPUs](https://mlsanity.com/paper/2308.00890/) (2023)
- [Demystifying Higher-Order Graph Neural Networks](https://mlsanity.com/paper/2406.12841/) (2024)
- [A Data-Centric Optimization Framework for Machine Learning](https://mlsanity.com/paper/2110.10802/) (2021)
- [Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence](https://mlsanity.com/paper/2407.15320/) (2024)
- [Accelerating SpMM Kernel with Cache-First Edge Sampling for Graph Neural Networks](https://mlsanity.com/paper/2104.10716/) (2021)
- [Scalable Graph Convolutional Network Training on Distributed-Memory Systems](https://mlsanity.com/paper/2212.05009/) (2022)
- [Knowledge Enhanced Graph Neural Networks for Graph Completion](https://mlsanity.com/paper/2303.15487/) (2023)
- [Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Services](https://mlsanity.com/paper/2307.01684/) (2023)
- [DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUs](https://mlsanity.com/paper/2411.16127/) (2024)
- [STen: Productive and Efficient Sparsity in PyTorch](https://mlsanity.com/paper/2304.07613/) (2023)
- [AdaptGear: Accelerating GNN Training via Adaptive Subgraph-Level Kernels on GPUs](https://mlsanity.com/paper/2305.17408/) (2023)
- [STAG: Enabling Low Latency and Low Staleness of GNN-based Services with Dynamic Graphs](https://mlsanity.com/paper/2309.15875/) (2023)
- [Performance Optimization of Deep Learning Sparse Matrix Kernels on Intel Max Series GPU](https://mlsanity.com/paper/2311.00368/) (2023)
- [Single-GPU GNN Systems: Traps and Pitfalls](https://mlsanity.com/paper/2402.03548/) (2024)
- [iSpLib: A Library for Accelerating Graph Neural Networks using Auto-tuned Sparse Operations](https://mlsanity.com/paper/2403.14853/) (2024)
- [Optimization of GNN Training Through Half-precision](https://mlsanity.com/paper/2411.01109/) (2024)
- [Analysis and Optimization of GNN-Based Recommender Systems on Persistent Memory](https://mlsanity.com/paper/2207.11918/) (2022)
- [Scalable Neural Network Training over Distributed Graphs](https://mlsanity.com/paper/2302.13053/) (2023)

## Abstract (arXiv)

Graph neural networks (GNNs) are gaining increasing popularity as a promising approach to machine learning on graphs. Unlike traditional graph workloads where each vertex/edge is associated with a scalar, GNNs attach a feature tensor to each vertex/edge. This additional feature dimension, along with consequently more complex vertex- and edge-wise computations, has enormous implications on locality and parallelism, which existing graph processing systems fail to exploit. This paper proposes FeatGraph to accelerate GNN workloads by co-optimizing graph traversal and feature dimension computation. FeatGraph provides a flexible programming interface to express diverse GNN models by composing coarse-grained sparse templates with fine-grained user-defined functions (UDFs) on each vertex/edge. FeatGraph incorporates optimizations for graph traversal into the sparse templates and allows users to specify optimizations for UDFs with a feature dimension schedule (FDS). FeatGraph speeds up end-to-end GNN training and inference by up to 32x on CPU and 7x on GPU.