A parallel algorithm for constructing Voronoi diagrams based on point‐set adaptive grouping

Wang, Jiechen; Cui, Can; Rui, Yikang; Cheng, Liang; Pu, Yingxia; Wu, Wenzhou; Yuan, Zhenyu

doi:10.1002/cpe.3005

Cited by 8 publications

(4 citation statements)

References 15 publications

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“…The heuristic partitioning method can be used to that end . The heuristic partitioning method recursively divides the dataset into two subsets until the number of subsets is equal to the given number . Compared with other regular decomposition methods, such as row‐wise decomposition, column‐wise decomposition, and cross decomposition, the use of the heuristic method results in fewer seam lines and reduces the cost of stitching .…”

Section: Adaptable Parallel Strategy For Raster‐to‐polygon Conversionmentioning

confidence: 99%

Adaptable parallel strategy to extract polygons from massive classified images on multi‐core clusters

Chen

Zhou

et al. 2016

Concurrency and Computation

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Summary It is always computing intensive and time consuming to extract polygons from massive classified images. Although parallel computing can improve the efficiency of geographical data processing, the performance of the conversion suffers from the trade‐off between data decomposition and result stitching. In this paper, we present an adaptable parallel strategy that accelerates the conversion process on a multi‐core cluster. The strategy improves the method of data decomposition and optimizes task scheduling. In our strategy, an adaptable decomposition method is used to partition raster data according to the data complexity of the raster dataset and computing capability of the computing nodes. Moreover, the hierarchical task scheduling optimizes task allocation and load balance among computing nodes for the procedure of parallel conversion and stitching. We implemented parallel algorithms based on a boundary linking algorithm using the adaptable parallel strategy and compared the performance with the performances of conventional parallel strategies. The results show that the processing time of experimental raster data was reduced from 1362.36 to 165.78 s and that a desirable speedup with the maximal value of 8.23 was achieved. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Adaptable Parallel Strategy For Raster‐to‐polygon Conversionmentioning

confidence: 99%

Adaptable parallel strategy to extract polygons from massive classified images on multi‐core clusters

Chen

Zhou

et al. 2016

Concurrency and Computation

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“…At present, no direct algorithm for parallel Voronoi diagram construction has been formulated specifically for distributed-memory platforms. So-called "stitching algorithms" perform distributed construction by piecing together local tessellations computed on separate processors [11,31]. However, such algorithms rely on a geometric decomposition of a stored global set of generators-a memory-intensive process for many scientific applications [17,28,18].…”

Section: Problem Statementmentioning

confidence: 99%

A new parallel algorithm for constructing Voronoi tessellations from distributed input data

Starinshak

Owen

Johnson

2014

Computer Physics Communications

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We present a new parallel algorithm for generating consistent Voronoi diagrams from distributed input data for the purposes of simulation and visualization. The algorithm functions by building upon any serial Voronoi tessellation algorithm. The output of such a serial tessellator is used to determine the connectivity of the distributed domains without any assumptions about how points are distributed across those domains, and then in turn to build the portion of the global tessellation local to each domain using information from that domains neighbors. The result is a generalized methodology for adding distributed capabilities to serial tessellation packages. Results from several two-dimensional tests are presented, including strong and weak scaling of its current implementation.

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“…shapes, distances, directions) of original objects (Chen et al ; Stoter et al ; Gröger and Plümer ). Since most geometric properties are abstracted to achieve high‐level invariance to certain topological transformations for algebraic expression and computation (Li et al ; Wang et al ), additional components (e.g. boundary, interior, and exterior) are required and the geometric properties of the original geographical objects (e.g.…”

Section: Introductionmentioning

confidence: 99%

Geometric Algebra Model for Geometry‐oriented Topological Relation Computation

Luo

Yuan

et al. 2015

Transactions in GIS

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Classical topological relation expressions and computations are primarily based on abstract algebra. In this article, the representation and computation of geometry-oriented topological relations (GOTR) are developed. GOTR is the integration of geometry and topology. The geometries are represented by blades, which contain both algebraic expressions and construction structures of the geometries in the conformal geometric algebra space. With the meet, inner, and outer products, two topology operators, the MeetOp and BoundOp operators, are developed to reveal the disjoint/intersection and inside/on-surface/outside relations, respectively. A theoretical framework is then formulated to compute the topological relations between any pair of elementary geometries using the two operators. A multidimensional, unified and geometry-oriented algorithm is developed to compute topological relations between geometries. With this framework, the internal results of the topological relations computation are geometries. The topological relations can be illustrated with clear geometric meanings; at the same time, it can also be modified and updated parametrically. Case studies evaluating the topological relations between 3D objects are performed. The result suggests that our model can express and compute the topological relations between objects in a symbolic and geometry-oriented way. The method can also support topological relation series computation between objects with location or shape changes.

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A parallel algorithm for constructing Voronoi diagrams based on point‐set adaptive grouping

Cited by 8 publications

References 15 publications

Adaptable parallel strategy to extract polygons from massive classified images on multi‐core clusters

Adaptable parallel strategy to extract polygons from massive classified images on multi‐core clusters

A new parallel algorithm for constructing Voronoi tessellations from distributed input data

Geometric Algebra Model for Geometry‐oriented Topological Relation Computation

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