Given a set of n areas spatially covering a geographical zone such as a province, forming contiguous regions from homogeneous neighboring areas satisfying a minimum threshold criterion over each region is an interesting NP-hard problem that has applications in various domains such as political science and GIS. We focus on a specific case, called Max-p regions problem, in which the main objective is to maximize the number of regions while keeping heterogeneity in each region as small as

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... The solution is broken into two phases: Construction phase and Optimization phase. We present a parallel implementation of the Max-p problem using Python multiprocessing library. By exploiting an intuitive data structure based on multi-locks, we achieve up 12-fold and 19-fold speeds up over the best sequential algorithm for the construction and optimization phases respectively. We provide extensive experimental results to verify our algorithm.