Abstract
In computer science, dependence analysis determines whether or not it is safe to parallelize statements in programs. In dealing with the data-intensive and computationally intensive spatial operations in processing massive volumes of geometric features, this dependence can be well utilized for exploiting the parallelism. In this paper, we propose a graph-based divide and conquer method for parallelizing spatial operations (GDCMPSO) on vector data. It can represent spatial data dependences in spatial operations through representing the vector features as graph vertices, and their computational dependences as graph edges. By this way, spatial operations can be parallelized in three steps: partitioning the graph into graph components with inter-component edges firstly, simultaneously processing multiple subtasks indicated by the graph components secondly and finally handling remainder tasks denoted by the inter-component edges. To demonstrate how it works, buffer operation and intersection operation under this paradigm are conducted. In a 12-core environment, the two spatial operations both gain obvious performance improvements, and the speedups are more than eight. The testing results suggest that GDCMPSO contributes to a method for parallelizing spatial operations and can greatly improve the computing efficiency on multi-core architectures.
Highlights
Spatial operations refer to the use of geometry functions to take spatial data as input, analyze the data, and produce output data that is the derivative of the analysis performed on the input data [1].From the perspectives of mathematics and computer science, the input is, or are, termed spatial operand, or operands, and the method to analyze the data is termed spatial operator
The main operators defined by the “Open Geospatial Consortium, Inc.” (OGC) are implemented by a set of geometry operations on geometry values in ArcGIS software, and they include the intersection of geometries, difference of geometries, union of geometries, symmetric difference of geometries, buffering zone of geometries, and convex hull of geometries [4], and a common characteristic is that all these operators create new data from input data [1]
The proposed method, GDCMPSO, provides an effective way to exploit the parallelism for the spatial operations when processing massive geometric features
Summary
Spatial operations refer to the use of geometry functions to take spatial data as input, analyze the data, and produce output data that is the derivative of the analysis performed on the input data [1].From the perspectives of mathematics and computer science, the input is, or are, termed spatial operand, or operands, and the method to analyze the data is termed spatial operator. The spatial operations have been widely used in many fields, including site selection [5,6], spatial decision making [7,8], crisis and disaster management [8,9], etc Another significant application of spatial operations is geographical conditions monitoring (GeoCM), which currently has become a research focus in China and other countries [10]. The multi-core processor has been widely used for the enhanced performance, reduced power consumption, and more efficient simultaneous processing of multiple tasks. It adopts scalable shared-memory multiprocessor architectures [19], and each processor can independently read and execute program instructions. Some high-level threading libraries, such as OpenMP (Open Multi-Processing) [21] and TBB
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