When sampling in parallel, the order in which you average divergence (before vs. after mixing output distributions) creates a dramatic gap in required rounds—from O((log n)²) to O(log log n)—revealing a fundamental complexity separation in parallel sampling efficiency.
This paper studies parallel sampling algorithms that generate multiple coordinates simultaneously, comparing two ways to measure how well the output distribution matches a target.