Data Preparation for photomask manufacturing is characterized by computational complexity that grows faster than the evolution of computer processor ability. Parallel processing generally addresses this problem and is an accepted mechanism for preparing mask data. One judges a parallel software implementation by total time, stability and predictability of computation. We apply several fundamental techniques to dramatically improve these metrics for a parallel, distributed MDP system. This permits the rapid, predictable computation of the largest mask layouts on conventional computing clusters.