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.
DrainSinkhorn is a verifier‑gated active‑packing layer that improves batched entropic optimal transport (EOT) by eliminating finished problems from subsequent Sinkhorn updates. It combines candidate‑axis packing, a one‑sided screen, verifier‑gated retirement, and physical compaction, while keeping the EOT objective, per‑instance map, and stopping rule unchanged. The method achieves state‑of‑the‑art execution speedups—up to 4.11× faster on MetroPT‑3 and 3.80× on ImageNet‑32 feature couplings—across multiple backends and tolerance settings.
whyItMatters":"The technique delivers significant runtime reductions for heterogeneous batched‑EOT workloads, enabling faster and more efficient optimal transport computations in practical machine‑learning pipelines."
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