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English(EN) FlashSinkhorn: IO-Aware Entropic Optimal Transport on GPU

FlashSinkhorn求解器加速GPU上的最优输运

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了FlashSinkhorn，一种新的GPU加速熵最优输运（EOT）求解器，可显著减少内存输入输出操作。通过重写稳定对数域Sinkhorn更新以模仿Transformer注意力中的归一化过程，FlashSinkhorn实现了融合内核，将数据流经片上SRAM。这种方法在A100 GPU上执行点云OT等任务时，与现有方法相比，前向传播速度提高了32倍，端到端速度提高了161倍。 AI

影响 这种IO感知的求解器可以加速各种依赖于最优输运的机器学习应用，可能提高效率和可扩展性。

排序理由 该集群包含一篇详细介绍机器学习任务新计算方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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FlashSinkhorn求解器加速GPU上的最优输运

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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Felix X. -F. Ye, Xingjie Li, An Yu, Ming-Ching Chang, Linsong Chu, Davis Wertheimer ·

    FlashSinkhorn: GPU上的IO感知熵最优传输

    arXiv:2602.03067v3 Announce Type: replace Abstract: Entropic optimal transport (EOT) via Sinkhorn iterations is widely used in modern machine learning, yet GPU solvers remain inefficient at scale. Tensorized implementations suffer quadratic HBM traffic from dense $n\times m$ inte…

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