动态批量更新标签传播，大幅提速半监督学习
DynLP: Parallel Dynamic Batch Update for Label Propagation in Semi-Supervised Learning
- GPU加速的动态批处理更新机制，仅重算相关子图
- 大模型上平均快13倍，峰值达102倍
- 适合增量数据场景的高效半监督学习应用
半监督学习旨在仅用少量标注数据推断类别标签。在基于图的半监督学习中，通常通过标签传播预测未标注节点的标签。然而，在真实应用场景中，数据常以批次形式增量到达。每次新批次到来时，重新运行传统标签传播算法并重新计算所有标签是冗余、计算密集且低效的。为解决现有高效标签传播更新方法的缺失，我们提出DynLP——一种新型的以GPU为中心的动态批处理并行标签传播算法，仅执行必要更新，将变化传播至相关子图，无需全量重算。通过利用GPU架构优化，该算法在大规模数据集上相较当前最优方法平均提升13倍，最高达102倍。
原文摘要 · Abstract (English)
Semi-supervised learning aims to infer class labels using only a small fraction of labeled data. In graph-based semi-supervised learning, this is typically achieved through label propagation to predict labels of unlabeled nodes. However, in real-world applications, data often arrive incrementally in batches. Each time a new batch appears, reapplying the traditional label propagation algorithm to recompute all labels is redundant, computationally intensive, and inefficient. To address the absence of an efficient label propagation update method, we propose DynLP, a novel GPU-centric Dynamic Batched Parallel Label Propagation algorithm that performs only the necessary updates, propagating changes to the relevant subgraph without requiring full recalculation. By exploiting GPU architectural optimizations, our algorithm achieves on average 13x and upto 102x speedup on large-scale datasets compared to state-of-the-art approaches.
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