HSampler: Optimizing Multi-GPU GNN Sampling with Collision-Avoid Selection

doi:10.1007/978-3-032-10459-5_1

2026-01-01

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DOI：10.1007/978-3-032-10459-5_1

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图神经网络（GNN）近年来在图学习任务中崭露头角。随着现实世界图规模日益增大，采样GNN被广泛应用于学术界和工业界，以替代在全图上训练。由于采样子图规模较小，训练阶段消耗的执行时间减少，数据准备阶段成为性能瓶颈，尤其是图采样阶段。尽管已提出多种采样方法以提高效率，但它们仍受限于偏差采样中高阶节点选择冲突带来的显著开销。本文提出HSampler，一个多GPU GNN采样系统。它通过重排序和滑动窗口选择采样子图，减少偏差采样中的重复尝试，从而提升图采样阶段的性能。在4 GPU节点上的评估表明，HSampler比DGL和P-3等最先进系统显著提升了2.1倍至6.2倍的性能。

Keyword:

GNN

Biased Sampling

Performance Optimizations

Parallelism

期刊

NETWORK AND PARALLEL COMPUTING, NPC 2025, PT I

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tsinghua university

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11.9W

论文数: 10.0W

被引数: 137

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