Journal Article10.1016/J.CAGEO.2015.09.003
Data decomposition method for parallel polygon rasterization considering load balancing
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TL;DR: Experimental results showed that DMPC could effectively parallelize different polygon rasterization algorithms and generally outperformed conventional decomposition methods in terms of parallel efficiency and load balancing and exhibited consistently better performance for different spatial distributions of polygons.
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About: This article is published in Computers & Geosciences. The article was published on 01 Dec 2015. The article focuses on the topics: Polygon & Point in polygon.
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Citations
The vector to raster conversion: (mis)use in geograhical information systems.
W.G.M. Knaap,van der +1 more
- 01 Jan 1995
TL;DR: In this article, a selection of eight geographical information systems convert vector test data to a raster pattern, giving results that differ both in number and in the location of assigned raster cells in the Raster pattern.
44
A parallel method to accelerate spatial operations involving polygon intersections
TL;DR: This work improved a method called boundary algebra filling to efficiently rasterize the input polygons and developed parallel strategies for different procedures in terms of workload decomposition and task scheduling, suggesting that the workload across different parallel processes can be balanced.
17
Using High-Performance Computing to Address the Challenge of Land Use/Land Cover Change Analysis on Spatial Big Data
TL;DR: A graph-based spatial decomposition is employed that represents the computational loads as graph vertices and edges and then uses a balanced graph partitioning to decompose the LUCC analysis on spatial big data and a stream scheduling method is developed to exploit the parallelism in data moving, clipping, overlay analysis, area calculation and transition matrix building.
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Rasterization Computing-Based Parallel Vector Polygon Overlay Analysis Algorithms Using OpenMP and MPI
TL;DR: Two types of parallel polygon overlay analysis algorithms are designed and implemented to determine the differences in efficiency between the RaPC algorithm and the Vatti algorithm under the open multi-processing and message passing interface parallel computing environments.
11
Improving Additive Manufacturing production planning: A sub-second pixel-based packing algorithm
TL;DR: Wang et al. as discussed by the authors proposed an efficient pixel-based additive manufacturing packing algorithm (PAMPA) which can tackle irregular packing sub-problems that allow hole filling and free rotation.
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References
Validity of the single processor approach to achieving large scale computing capabilities
Gene Myron Amdahl
- 18 Apr 1967
TL;DR: In this paper, the authors argue that the organization of a single computer has reached its limits and that truly significant advances can be made only by interconnection of a multiplicity of computers in such a manner as to permit cooperative solution.
4K
•Book
Introduction to Geographic Information Systems
Kang-Tsung Chang
- 01 Jan 2001
TL;DR: This paper presents a meta-modelling architecture suitable for GIS models and Modeling of vector data models, and some examples show how this architecture can be modified for distributed systems.
889
The point in polygon problem for arbitrary polygons
Kai Hormann,Alexander Agathos +1 more
TL;DR: It is shown by mathematical means that both concepts for solving the point in polygon problem for arbitrary polygons are very closely related, thereby developing a first version of an algorithm for determining the winding number.
Distributed frameworks and parallel algorithms for processing large-scale geographic data
Ken A. Hawick,Paul Coddington,H. A. James +2 more
- 01 Oct 2003
TL;DR: A historical review of work in this area over the last decade leads us to believe parallel computing will continue to play an important role in GIS and speculate on algorithmic and systems issues for the future.