Parallelization Strategies to Speed-Up Computations for Terrain Analysis on Multi-Core Processors

Abstract

Efficient computation of regional land-surface parameters for large-scale digital elevation models becomes more and more important, in particular for webbased applications. This paper studies the possibilities of decreasing computing time for such tasks by parallel processing using multi-threads on multi-core processors. As an example of calculations of regional land-surface parameters we investigate the computation of flow directions and propose a modified D8 algorithm using an extended neighborhood. In this paper, we discuss two parallelization strategies, one based on a spatial decomposition, the other based on a two-phase approach. Three datasets of high resolution digital elevation models with different geomorphological types of landscapes are used in our evaluation. While local surface parameters allow for an almost ideal speed-up, the situation is different for the calculation of non-local parameters due to data dependencies. Nevertheless, still a significant decrease of computation time has been achieved. A task pool-based strategy turns out to be more efficient for calculations on datasets with many data dependencies.