Abstract
Abstract. Delaunay triangulated irregular network (D-TIN) has been widely used in various fields and also played an increasingly important role on map generalization. But for massive data processing, current D-TIN algorithm is still not efficient enough to meet the requirements of map generalization. Data partitioning is an important step of parallel algorithm design. The load balance and efficiency of data partitioning is the precondition of improving parallel algorithm efficiency. For aggregated distributed point sets, the traditional Delaunay Triangulation parallel algorithm can’t ensure the balance and the execution’s efficiency of the partitioning result. The paper introduces a partitioning method using dynamic strips aiming to guarantee the computing load balance. We tested the speed-up of the D-TIN parallel algorithm using different type of point sets and the results of the experiments shows that the method of dynamic strips partitioning can help to get high and stable speed-up and the data distributional pattern and size has less influence to it. The paper realizes a mesh simplification algorithm based on parallel D-TIN and compares the efficiency based on parallel and serial D-TIN.
Highlights
As an important tool for geometry calculation, Delaunay triangulated irregular network (D-TIN) (Delaunay Triangulated Irregular Network) has been widely used in various fields
Shamos and Hoey first introduces the divide-and-conquer method, Lewis put forward the incremental insertion method and Lee, Schachter carried out improvement and perfection, Green first realized triangulation growth method
The paper realizes a mesh simplification algorithm based on parallel D-TIN and compares its efficiency based on parallel and serial D-TIN
Summary
As an important tool for geometry calculation, D-TIN (Delaunay Triangulated Irregular Network) has been widely used in various fields. The present paper describes a new parallel D-TIN construction method based on dynamic strips data partitioning. This method proposed the division principle and can meet requirements of relatively balanced division results for different distributing types of points set data, including gathering point set data. Data partitioning for traditional D-TIN parallel algorithms can obtain relatively balanced division results and high efficiency when it is used for the point set in uniform density.
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