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Accelerating Graph Sampling for Graph Machine Learning using GPUs

arXiv 2009.06693 ↗First posted 14 Sep 2020cs.DC cs.LGcited by 29

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Abstract (arXiv)

Representation learning algorithms automatically learn the features of data. Several representation learning algorithms for graph data, such as DeepWalk, node2vec, and GraphSAGE, sample the graph to produce mini-batches that are suitable for training a DNN. However, sampling time can be a significant fraction of training time, and existing systems do not efficiently parallelize sampling. Sampling is an embarrassingly parallel problem and may appear to lend itself to GPU acceleration, but the irregularity of graphs makes it hard to use GPU resources effectively. This paper presents NextDoor, a system designed to effectively perform graph sampling on GPUs. NextDoor employs a new approach to graph sampling that we call transit-parallelism, which allows load balancing and caching of edges. NextDoor provides end-users with a high-level abstraction for writing a variety of graph sampling algorithms. We implement several graph sampling applications, and show that NextDoor runs them orders of magnitude faster than existing systems.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Sep 2020.

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Citing papers per year: 2021: 2; 2022: 7; 2023: 9; 2024: 6; 2025: 2; 2026: 3.

29 papers.

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  1. 2021BGL: GPU-Efficient GNN Training by Optimizing Graph Data I/O and Preprocessingcited by 50
  2. 2022PaSca: a Graph Neural Architecture Search System under the Scalable Paradigmcited by 37
  3. 2022Distributed Graph Neural Network Training: A Surveycited by 32
  4. 2022Towards Training Billion Parameter Graph Neural Networks for Atomic Simulationscited by 25
  5. 2022MariusGNN: Resource-Efficient Out-of-Core Training of Graph Neural Networkscited by 19
  6. 2023The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Surveycited by 13
  7. 2021Scalable Graph Neural Network Training: The Case for Samplingcited by 11
  8. 2023Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awarenesscited by 11
  9. 2024Graph Neural Network Training Systems: A Performance Comparison of Full-Graph and Mini-Batchcited by 10
  10. 2023GSplit: Scaling Graph Neural Network Training on Large Graphs via Split-Parallelismcited by 6
  11. 2023Distributed Matrix-Based Sampling for Graph Neural Network Trainingcited by 6
  12. 2023NeutronOrch: Rethinking Sample-based GNN Training under CPU-GPU Heterogeneous Environmentscited by 5
17 more
  1. 2023GNNFlow: A Distributed Framework for Continuous Temporal GNN Learning on Dynamic Graphscited by 5
  2. 2023FreshGNN: Reducing Memory Access via Stable Historical Embeddings for Graph Neural Network Trainingcited by 4
  3. 2024GraphScale: A Framework to Enable Machine Learning over Billion-node Graphscited by 4
  4. 2022Characterizing the Efficiency of Graph Neural Network Frameworks with a Magnifying Glasscited by 3
  5. 2024FastGL: A GPU-Efficient Framework for Accelerating Sampling-Based GNN Training at Large Scalecited by 3
  6. 2022Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPUcited by 2
  7. 2023Distributed Graph Embedding with Information-Oriented Random Walkscited by 2
  8. 2024TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learningcited by 2
  9. 2022Hardware/Software Co-Programmable Framework for Computational SSDs to Accelerate Deep Learning Service on Large-Scale Graphscited by 1
  10. 2024Single-GPU GNN Systems: Traps and Pitfallscited by 1
  11. 2024LSM-GNN: Large-scale Storage-based Multi-GPU GNN Training by Optimizing Data Transfer Schemecited by 1
  12. 2025Scaling Graph Neural Networks for Particle Track Reconstructioncited by 1
  13. 2023Scalable Neural Network Training over Distributed Graphscited by 0
  14. 2025Armada: Memory-Efficient Distributed Training of Large-Scale Graph Neural Networkscited by 0
  15. 2026Grappa: Gradient-Only Communication for Scalable Graph Neural Network Trainingcited by 0
  16. 2026NOMAD: Generating Embeddings for Massive Distributed Graphscited by 0
  17. 2026Directly Optimizing Mean Demographic Parity for Nonlinear Regressioncited by 0