arXiv:cs.AI· Felix X. -F. Ye, Yu Chin Fabian Lim, Naigang Wang, Davis Wertheimer·· 5 小时前AI 评分44
FlashSinkhorn 2:块稀疏熵最优传输求解器
FlashSinkhorn 2: Block-Sparse Entropic Optimal Transport
AI 导读
FlashSinkhorn 2(FS2)是面向低维点云平方欧氏代价的熵最优传输求解器,通过粗粒度质心求解与块稀疏精修两阶段耦合,在单张 A100 上以低于 0.01 的全粒子边际残差求解两个 1.34×10^8 粒子测度间的离散 EOT,耗时不到 2.5 小时。
正文
Abstract:Streaming GPU solvers for entropic optimal transport (EOT), such as FlashSinkhorn, avoid storing the dense kernel but still evaluate all $n\times m$ point pairs in every Sinkhorn iteration. We present \textbf{FlashSinkhorn~2} (FS2), a solver for squared-Euclidean cost on low-dimensional point clouds that solves large discrete EOT problems to a prescribed marginal residual on a single GPU by coupling two stages. A coarse stage solves on cell centroids, lifts the potentials to every point and, when a sampled marginal check rejects the lift, continues on the centroids, replacing most point-level updates. A block-sparse fine stage then removes the centroid error that coarse updates cannot. Its Morton-ordered blocks support screening and fused tensor-core execution, and a threshold set by the block masses bounds each omitted tile's contribution to every row and column. On synthetic benchmarks, FS2 reaches the target residual on all 32 problems and GeomLoss multiscale on 10. On one A100, FS2 solves discrete EOT between two $1.34\times10^8$-particle measures from a cosmological $N$-body simulation, at an entropic blur equal to the mean interparticle distance, to an all-particle marginal residual below 0.01 in under 2.5 hours. To our knowledge, it is the largest discrete EOT problem solved to this accuracy within hours. For reproducibility, we release an open-source implementation at this https URL
| Subjects: | Artificial Intelligence (cs.AI); Instrumentation and Methods for Astrophysics (astro-ph.IM); Numerical Analysis (math.NA) |
| Cite as: | arXiv:2610.02395 [cs.AI] |
| (or arXiv:2610.02395v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02395 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Felix Xiaofeng Ye [view email]
[v1]
Thu, 1 Oct 2026 19:22:49 UTC (162 KB)
来源:arXiv:cs.AI · arxiv.org