General-purpose optimization methods for parallelization of digital terrain analysis based on cellular automata

Solving traditional spatial analysis problems benefits from high performance geo-computation powered by parallel computing. Digital Terrain Analysis (DTA) is a typical example of data and computationally intensive spatial analysis problems and can be improved by parallelization technologies. Previous work on this topic has mainly focused on applying optimization schemes for specific DTA case studies. The task addressed in this paper, in contrast, is to find optimization methods that are generally applicable to the parallelization of DTA. By modeling a complex DTA problem with Cellular Automata (CA), we developed a temporal model that can describe the time cost of the solution. Three methods for optimizing different components in the temporal model are proposed: (1) a parallel loading/writing method that can improve the IO efficiency; (2) a best cell division method that can minimize the communication time among processes; and (3) a communication evolution overlapping method that can reduce the total time of evolutions and communications. The feasibilities and practical efficiencies of the proposed methods have been verified by comparative experiments conducted on an elevation dataset from North America using the Slope of Aspect (SOA) as an example of a general DTA problem. The results showed that the parallel performance of the SOA can be improved by applying the proposed methods individually or in an integrated fashion.