We study the shared randomness needed to sample a uniform permutation in parallel output rounds. Coordinates committed in the same round are independent conditional on the complete history and the current shared choice. Previous outputs are public; local computation and private randomness are free. We charge each shared choice by the logarithm of its conditional support size and sum these costs along the worst execution path. At every fixed round density $ρ\in(0,1)$, the optimal limiting total-variation error under any $o(n)$ shared budget matches the conditional-marginal optimum, even with arbitrary local kernels and adaptive coordination. A fixed improvement requires linear cost. For exact sampling in $R=Θ(n)$ rounds, the optimum is $nΦ(R/n)+O(\log n)$, where $Φ$ linearly interpolates $Φ(1/q)=\log q$. A protocol attaining this rate uses an initial seed and at most one further binary shared choice. When $\lceil n/2\rceil\le R\le n$, the finite optimum is $\log\binom{2(n-R)}{n-R}$, attained by an initial seed alone. The approximate converse uses an entropy inequality for sparse injections to control overlap with the target. The exact construction reuses randomness retained in a shared label partition after the first-stage outputs are exposed.

翻译：暂无翻译