AI RESEARCH
Fast Log-Domain Sinkhorn Optimal Transport with Warp-Level GPU Reductions
arXiv CS.LG
•
ArXi:2605.00837v1 Announce Type: new Entropic regularized optimal transport (OT) via the Sinkhorn algorithm has become a fundamental tool in machine learning, yet existing implementations either suffer from numerical instability for small regularization parameters or incur significant overhead from deep learning frameworks. We present FastSinkhorn, a lightweight, native CUDA implementation of the log-domain Sinkhorn algorithm that combines warp-level shuffle reductions with shared-memory tiling to achieve high GPU utilization without sacrificing numerical stability.