这篇论文提出了TemporalSinkhorn，用并行时间策略让Sinkhorn算法在动态最优传输中提速几倍，在Flow Matching上实测3倍以上，实用性很强。
论文提出TemporalSinkhorn，一种并行时间执行器，批处理未来候选解并在不牺牲精度前提下进行修复。在4块A100 GPU上，n=2048的60次五种子网格测试中，遗忘引导里程碑相比逐次审计获得1.15x-1.47x加速。在Flow Matching小批量流上，时间执行相比顺序传递加速3.054x-3.632x，无容差违规。在RTX 4060笔记本GPU上固定核测试获得4.315x几何平均加速。
Certified Parallel-in-Time Sinkhorn for Dynamic Entropic Optimal Transport
Dynamic applications, including optimal-transport Flow Matching, repeatedly solve related entropic optimal transport problems, yet conventional distributed Sinkhorn processes frames sequentially and synchronizes after every iteration. We present TemporalSinkhorn, a parallel-in-time executor that batches future candidates and their repairs without making output accuracy speculative. A centered, row-sharded certificate accepts only a deterministic safe prefix. The remaining candidates share packed Sinkhorn updates; an online projective forgetting rate places audit milestones, while a posteriori residual checks recover from every depth underestimate. Prediction can therefore change work placement but cannot authorize an inaccurate output. On 4 A100 GPUs, a 60-run, five-seed grid at n = 2048 shows that forgetting-guided milestones reduce wall time by 1.15x-1.47x relative to auditing every packed iteration in five statistically resolved regime cells. Against a sequential soft c-transform warm start, temporal execution is 1.42x-3.55x faster across six synthetic streams, with zero marginal-tolerance violations. On Flow Matching minibatch streams, temporal execution is 3.054x-3.632x faster than sequential carry at n = 2048, with no tolerance violations. A separate fixed-kernel test on an RTX 4060 Laptop GPU gives a 4.315x geometric-mean speedup. These are complementary deployment studies rather than a controlled hardware comparison. End-to-end Flow Matching integration, optimized-solver comparisons, and multi-node validation remain open.