mlsanityData to 7 Oct 2026

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

arXiv 2008.11359 ↗First posted 26 Aug 2020cs.LG cs.DCcited by 36

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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.

Key facts. Cited by 36 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Aug 2020.

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Citing papers per year: 2021: 4; 2022: 8; 2023: 13; 2024: 6; 2025: 4; 2026: 1.

36 papers.

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  1. 2022TorchSparse: Efficient Point Cloud Inference Enginecited by 72
  2. 2021DistGNN: Scalable Distributed Training for Large-Scale Graph Neural Networkscited by 59
  3. 2022SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learningcited by 47
  4. 2022Distributed Graph Neural Network Training: A Surveycited by 32
  5. 2022Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysiscited by 29
  6. 2021Motif Prediction with Graph Neural Networkscited by 18
  7. 2023A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardwarecited by 18
  8. 2023The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Surveycited by 13
  9. 2022Neural Graph Databasescited by 11
  10. 2022GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Serverscited by 11
  11. 2023HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformerscited by 10
  12. 2023Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accessescited by 6
24 more
  1. 2023Tango: rethinking quantization for graph neural network training on GPUscited by 4
  2. 2024Demystifying Higher-Order Graph Neural Networkscited by 4
  3. 2021A Data-Centric Optimization Framework for Machine Learningcited by 3
  4. 2021Accelerating SpMM Kernel with Cache-First Edge Sampling for Graph Neural Networkscited by 2
  5. 2022Scalable Graph Convolutional Network Training on Distributed-Memory Systemscited by 2
  6. 2023Knowledge Enhanced Graph Neural Networks for Graph Completioncited by 2
  7. 2023Serving Graph Neural Networks With Distributed Fog Servers For Smart IoT Servicescited by 2
  8. 2024Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligencecited by 2
  9. 2024DF-GNN: Dynamic Fusion Framework for Attention Graph Neural Networks on GPUscited by 2
  10. 2023STen: Productive and Efficient Sparsity in PyTorchcited by 1
  11. 2023AdaptGear: Accelerating GNN Training via Adaptive Subgraph-Level Kernels on GPUscited by 1
  12. 2023Performance Optimization of Deep Learning Sparse Matrix Kernels on Intel Max Series GPUcited by 1
  13. 2024Single-GPU GNN Systems: Traps and Pitfallscited by 1
  14. 2024iSpLib: A Library for Accelerating Graph Neural Networks using Auto-tuned Sparse Operationscited by 1
  15. 2024Optimization of GNN Training Through Half-precisioncited by 1
  16. 2022Analysis and Optimization of GNN-Based Recommender Systems on Persistent Memorycited by 0
  17. 2023Scalable Neural Network Training over Distributed Graphscited by 0
  18. 2023Rosko: Row Skipping Outer Products for Sparse Matrix Multiplication Kernelscited by 0
  19. 2023STAG: Enabling Low Latency and Low Staleness of GNN-based Services with Dynamic Graphscited by 0
  20. 2025SparseTransX: Efficient Training of Translation-Based Knowledge Graph Embeddings Using Sparse Matrix Operationscited by 0
  21. 2025The Ubiquitous Sparse Matrix-Matrix Productscited by 0
  22. 2025Panther: A Cost-Effective Privacy-Preserving Framework for GNN Training and Inference Services in Cloud Environmentscited by 0
  23. 2025Morphling: Fast, Fused, and Flexible GNN Training at Scalecited by 0
  24. 2026Permutation-Equivariant Flow Matching for Alignment-Free Neural Weight Generationcited by 0