Parallel terrain triangulation

Puppo, Enrico; Davis, Larry S.; Menthon, D. De; Teng, Yuan-Hsiang

doi:10.1080/02693799408901989

Cited by 25 publications

(10 citation statements)

References 30 publications

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“…Research on parallel GIS began more than 20 years ago, and many studies on geospatial applications of parallel computing have been conducted, for example, in transportation and land-use modeling (Harris 1985), spatial data handling and analysis (Sandu andMarble 1988, Li 1992), least cost path (Smith et al 1989), polygon overlay (Wang 1993), terrain analysis (Peucker and Douglas 1975, Rokos and Armstrong 1992, Puppo et al 1994, Kidner et al 1997, geostatistics (Armstrong and Marciano 1993-1997, Cramer and Armstrong 1997, Kerry and Hawick 1997, Wang and Armstrong 2003, and earth observation (Aloisio andCafaro 2003, Ananthanarayan et al 2003). The research has moved in two main directions: parallelizing existing computationally intensive GIS operations and developing new geospatial analytical methods using additional computational power (Clematis et al 2003).…”

Section: Parallel Computing For Geospatial Processingmentioning

confidence: 99%

A general-purpose parallel raster processing programming library test application using a geographic cellular automata model

Guan

Clarke

2010

International Journal of Geographical Information Science

115

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A general-purpose parallel raster processing programming library test application using a geographic cellular automata model" (2010 Department of Geography, University of California, Santa Barbara, Santa Barbara, CA, USA Abstract A general-purpose parallel raster processing programming library (pRPL) was developed and applied to speed up a commonly used cellular automaton model with known tractability limitations. The library is suitable for use by geographic information scientists with basic programming skills, but who lack knowledge and experience of parallel computing and programming. pRPL is a general-purpose programming library that provides generic support for raster processing, including local-scope, neighborhood-scope, regional-scope, and global-scope algorithms as long as they are parallelizable. The library also supports multilayer algorithms. Besides the standard data domain decomposition methods, pRPL provides a spatially adaptive quad-tree-based decomposition to produce more evenly distributed workloads among processors. Data parallelism and task parallelism are supported, with both static and dynamic load-balancing. By grouping processors, pRPL also supports data-task hybrid parallelism, i.e., data parallelism within a processor group and task parallelism among processor groups. pSLEUTH, a parallel version of a well-known cellular automata model for simulating urban land-use change (SLEUTH), was developed to demonstrate full utilization of the advanced features of pRPL. Experiments with real-world data sets were conducted and the performance of pSLEUTH measured. We conclude not only that pRPL greatly reduces the development complexity of implementing a parallel raster-processing algorithm, it also greatly reduces the computing time of computationally intensive raster-processing algorithms, as demonstrated with pSLEUTH.

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Section: Parallel Computing For Geospatial Processingmentioning

confidence: 99%

A general-purpose parallel raster processing programming library test application using a geographic cellular automata model

Guan

Clarke

2010

International Journal of Geographical Information Science

115

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“…The massive amount of ever-growing spatial data and the high computational complexity of many spatial analysis methods often lead to an infeasible length of computing time and vast memory space, which has become a pressing challenge and bottleneck for large-scale GeoComputation. High-performance computing (HPC) technologies, especially parallel computing, have been used in many geospatial studies as a solution to computationally intensive and data-intensive problems such as spatial data handling and analysis (Sandu and Marble 1988;Li 1992), least cost path (Smith et al 1989), polygon overlay (Wang 1993), terrain analysis (Rokos and Armstrong 1992;Puppo et al 1994;Kidner et al 1997;Zhao et al 2013), land use modeling (Li et al 2010), and geostatistics (Armstrong and Marciano 1993, 1997Cramer and Armstrong 1997;Kerry and Hawick 1998;Wang and Armstrong 2003;Guan et al 2011).…”

Section: Parallel Geospatial Computingmentioning

confidence: 99%

“…), polygon overlay (Wang ), terrain analysis (Rokos and Armstrong ; Puppo et al. ; Kidner et al. ; Zhao et al.…”

Section: Introductionmentioning

confidence: 99%

pRPL 2.0: Improving the Parallel Raster Processing Library

Guan

Zeng

Gong

et al. 2014

Transactions in GIS

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This article presents an improved parallel Raster Processing Library -pRPL version 2.0. Since the release of version 1.0, a series of modifications has been made in pRPL to improve its usability, flexibility, and performance. While retaining some of the key features of pRPL, the new version has gained several new features: (1) a new DataManager class has been added for integrated data management, and to facilitate data decomposition, assignment mapping, data distribution, Transition execution, and load-balancing; (2) a GDAL-based raster data I/O mechanism has been added to support various geospatial raster data formats, and provide centralized and pseudo parallel I/O modes; and (3) a static load-balancing mode and a dynamic load-balancing mode using the task-farming technique are provided. A parallel zonal statistics tool and a parallel Cellular Automata model were developed to demonstrate the usability and performance of pRPL 2.0. The experiments using the California datasets showed that the performance altered when different pRPL options (i.e. load-balancing mode, I/O mode and writer mode) were used for different algorithms, datasets, and varying numbers of processes.

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“…Puppo et al [38] present another parallel algorithm based on incremental insertion. Their algorithm is not devoted to Delaunay triangulation only but rather to building a triangulated irregular network from a dense regular grid of points.…”

Section: Construction Of the Delaunay Triangulationmentioning

confidence: 99%

Practically oriented parallel Delaunay triangulation in E2 for computers with shared memory

Kohout

Kolingerová

Žára

2004

Computers & Graphics

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This paper describes two simple and efficient parallel algorithms for the construction of the Delaunay triangulation ðDTðSÞÞ in E 2 by randomized incremental insertion. The construction of the DTðSÞ is one of the fundamental problems in computer graphics. The proposed algorithms are designed for parallel systems several processors and with shared memory. Such a hardware configuration (especially the case with two-processors) became widely available in the last few years thanks to low prices at present, but there is still a lack of parallel algorithms that are simple to implement and efficient enough to be an attractive alternative to existing serial algorithms. We have implemented both new algorithms in C++ and tested them on workstations with up to four processors. Thanks to memory caching we noticed several times even super-linear speed-up compared with the reference sequential algorithm. r

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Parallel terrain triangulation

Cited by 25 publications

References 30 publications

A general-purpose parallel raster processing programming library test application using a geographic cellular automata model

A general-purpose parallel raster processing programming library test application using a geographic cellular automata model

pRPL 2.0: Improving the Parallel Raster Processing Library

Practically oriented parallel Delaunay triangulation in E2 for computers with shared memory

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