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FlashSinkhorn 2: Block-Sparse Entropic Optimal Transport

arXiv 2610.02395 ↗First posted 1 Oct 2026cs.AI astro-ph.IM cs.NAcited by 0

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Abstract (arXiv)

Streaming GPU solvers for entropic optimal transport (EOT), such as FlashSinkhorn, avoid storing the dense kernel but still evaluate all n× m point pairs in every Sinkhorn iteration. We present 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 1.34×108-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 https://github.com/ot-triton-lab/flash-sinkhorn

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 41 references that are in the map. First posted on arXiv 1 Oct 2026.

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References · 25

  1. 2022FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awarenesscited by 3,673
  2. 2013Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distancescited by 2,316
  3. 2018Computational Optimal Transportcited by 780
  4. 2024Flex Attention: A Programming Model for Generating Optimized Attention Kernelscited by 185
  5. 2018Online normalizer calculation for softmaxcited by 162
  6. 2020Kernel Operations on the GPU, with Autodiff, without Memory Overflowscited by 104
  7. 2016Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problemscited by 73
  8. 2021Low-Rank Sinkhorn Factorizationcited by 67
  9. 2018Massively scalable Sinkhorn distances via the Nystr"om methodcited by 41
  10. 2019Screening Sinkhorn Algorithm for Regularized Optimal Transportcited by 20
  11. 2017Multiscale Strategies for Computing Optimal Transportcited by 19
  12. 2021Quantized Gromov-Wassersteincited by 19
13 more
  1. 2015A Sparse Multi-Scale Algorithm for Dense Optimal Transportcited by 18
  2. 2017Overrelaxed Sinkhorn-Knopp Algorithm for Regularized Optimal Transportcited by 18
  3. 2021Fast and Scalable Optimal Transport for Brain Tractogramscited by 12
  4. 2021Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and Morecited by 11
  5. 2024Accelerating Sinkhorn Algorithm with Sparse Newton Iterationscited by 11
  6. 2026FlashSinkhorn: IO-Aware Entropic Optimal Transport on GPUcited by 11
  7. 2025Hierarchical Refinement: Optimal Transport to Infinity and Beyondcited by 9
  8. 2020A note on overrelaxation in the Sinkhorn algorithmcited by 7
  9. 2023Efficient and Accurate Optimal Transport with Mirror Descent and Conjugate Gradientscited by 7
  10. 2025A Truncated Newton Method for Optimal Transportcited by 6
  11. 2020Domain decomposition for entropy regularized optimal transportcited by 5
  12. 2022Nonequispaced Fast Fourier Transform Boost for the Sinkhorn Algorithmcited by 4
  13. 2026cuRegOT: A GPU-Accelerated Solver for Entropic-Regularized Optimal Transportcited by 1

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