Vector target set balanced partitioning method aiming at spatial measure and direction relation concurrent computation

Abstract

The invention discloses a vector target set balanced partitioning method aiming at spatial measure and direction relation concurrent computation. The vector target measure (vector target length and area computation) and direction relation (direction relation among vector targets) concurrent computation belongs to a compute-intensive algorithm, i.e., all points of geometric objects in vector target sets participate in the computation during a target measure and direction relation computation process. Therefore, the load balance of tasks in all processes needs give consideration to the number of vertexes of the vector target sets. Therefore, the balanced partitioning method giving consideration to the number of vertexes of the vector target sets is adopted aiming at the vector target measure and direction relation to evenly partition the vector target sets to all the processes, so that the load of the tasks of the vector target sets in all the processes is balanced. By utilizing the vector target set balanced partitioning method aiming at the spatial measure and direction relation concurrent computation disclosed by the invention, the high balance of inter-process computation loads can be realized, and further, the algorithm efficiency is improved. A high-efficiency data partitioning method is provided to the development and services of spatial measure and direction relation software for mass data in a single-machine multi-core or many-core high-performance cluster environment.

Description

technical field [0001] The invention belongs to the field of parallel computing, and in particular relates to a vector target set balanced division method for parallel computing of vector target space metrics and direction relations. Background technique [0002] Spatial metric relations and spatial direction relations both belong to spatial relations. The metric relationship includes the area, perimeter, and distance between space objects, which are used to describe the characteristics of the space objects themselves; the direction relationship expresses the orientation between two space objects, and is usually used to describe the spatial orientation between distant objects. . Spatial measurement and direction relations play a very important role in spatial reasoning and spatial query, and are also important contents of geographic information systems. With the explosive growth of spatial data volume, the traditional serial algorithm of spatial measurement and direction r...

AI Technical Summary

Problems solved by technology

At present, the vector target set division method of parallel algorithm mainly has round-robin division method. [1] , Scope division method [1-3] , hash division method [1，4] , mixed division method [1，5] , space curve division method [6，7] etc., but none of these existing partitioning methods can ensure the load balance of tasks in each process of parallel computing according to the characteristics of the metric and direction relation algorithms, which affects the computing efficiency

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