A System for GIS Polygonal Overlay Computation on Linux Cluster - An Experience and Performance Report

Agarwal, Dinesh; Puri, Satish; He, Xi; Prasad, Sushil K.

doi:10.1109/ipdpsw.2012.180

Cited by 31 publications

(22 citation statements)

References 9 publications

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“…The time cost of DWSI is in positive proportion to the number of features in the two overlapped layers and is highly related with the intersection situations between them. However, the time cost of DWSI is limited by the inherent defects of R-tree, whose search efficiency is in a linear drop with the growing number of features when the MBRs of the features are overlapped seriously in the R-tree indexing structure (BECKMANN, 1990). Hence, DWSI will encounter a similar problem in addressing such problems.…”

Section: The Dwsi Methodsmentioning

confidence: 99%

“…There are a variety of data decomposition strategies for parallelizing the polygon overlay operation without topological relationships, such as data partitioning by regular grids (WAUGH and HOPKINS, 1992) and strips (MINETER et al, 1999), feature sequences (AGARWAL et al, 2012), and expected balanced workloads (ZHAO and ZHOU, 2013). Shi (2012) has pointed out that different polygon overlay operations involve different relationships between polygons from the base layer and overlap layer.…”

Section: Related Workmentioning

confidence: 99%

“…R-tree implemented in memory can be extended dynamically in a convenient way and can support the spatial indexing of a multi-dimensional dataset. The search efficiency of R-tree is higher than that of the grid index and the Quadtree, and the complexity is lower than its variants, such as R*-tree (BECKMANN et al, 1990), HILBERT R-tree (KAMEL et al, 1994), and CR-tree (KIM et al, 2001). …”

Section: Memory Limit and R-treementioning

confidence: 99%

“…Polygon overlay is a key category of spatial analysis operations in GIS, and polygon clipping is one of the basic problem which always can be computationally intensive (GOODCHILD, 1977;WANG, 1993;SHI, 2012;AGARWAL et al, 1 6 1 2012). The Weiler-Atherton algorithm (WEILER and ATHERTON, 1977), the Vatti algorithm (VATTI, 1992), and the Greiner-Horman algorithm (GREINER and HORMANN, 1998) are three acknowledged polygon clipping algorithms that can generate results in a limited amount of time.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

DWSI: an approach to solving the polygon intersection-spreading problem with a parallel union algorithm at the feature layer level

Fan

Zhou

et al. 2014

Bol. Ciênc. Geod.

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A dual-way seeds indexing (DWSI) method based on R-tree and the Open Geospatial Consortium (OGC) simple feature model was proposed to solve the polygon intersection-spreading problem. The parallel polygon union algorithm based on the improved DWSI and the OpenMP parallel programming model was developed to validate the usability of the data partition method. The experimental results reveal that the improved DWSI method can implement a robust parallel task partition by overcoming the polygon intersection-spreading problem. The parallel union algorithm applied DWSI not only scaled up the data processing but also speeded up the computation compared with the serial proposal, and it showed a higher computational efficiency with higher speedup benchmarks in the treatment of larger-scale dataset. Therefore, the improved DWSI can be a potential approach to parallelizing the vector data overlay algorithms based on the OGC simple data model at the feature layer level.

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Section: The Dwsi Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Memory Limit and R-treementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

DWSI: an approach to solving the polygon intersection-spreading problem with a parallel union algorithm at the feature layer level

Fan

Zhou

et al. 2014

Bol. Ciênc. Geod.

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“…A solution to this problem can be what we have implemented in our design of Crayons system for Linux clusters [17]. In this case, the producer creates a limited number of tasks and assigns them to the available workers.…”

Section: Queues Without Head or Tail And Message Visibility Timeoutmentioning

confidence: 99%

Lessons Learnt from the Development of GIS Application on Azure Cloud Platform

Agarwal

Prasad

2012

2012 IEEE Fifth International Conference on Cloud Computing

Self Cite

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Spatial overlay processing is a widely used computeintensive GIS application that involves aggregation of two or more layers of maps to facilitate intelligent querying on the collocated output data. When large GIS data sets are represented in polygonal (vector) form, spatial analysis runs for extended periods of time, which is undesirable for time-sensitive applications such as emergency response. We have, for the first time, created an open-architecture-based system named Crayons for Azure cloud platform using state-of-the-art techniques. During the course of development of Crayons system, we faced numerous challenges and gained invaluable insights into Azure cloud platform, which are presented in detail in this paper. The challenges range from limitations of cloud storage and computational services to the choices of tools and technologies used for high performance computing (HPC) application design. We report our findings to provide concrete guidelines to an eScience developer for 1) choice of persistent data storage mechanism, 2) data structure representation, 3) communication and synchronization among nodes, 4) building robust failsafe applications, and 5) optimal cost-effective utilization of resources. Our insights into each challenge faced, the solution to overcome it, and the discussion on the lessons learnt from each challenge can be of help to eScience developers starting application development on Azure and possibly other cloud platforms.

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OpenACC Based GPU Parallelization of Plane Sweep Algorithm for Geometric Intersection

Paudel

Puri

2019

Accelerator Programming Using Directives

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A System for GIS Polygonal Overlay Computation on Linux Cluster - An Experience and Performance Report

Cited by 31 publications

References 9 publications

DWSI: an approach to solving the polygon intersection-spreading problem with a parallel union algorithm at the feature layer level

DWSI: an approach to solving the polygon intersection-spreading problem with a parallel union algorithm at the feature layer level

Lessons Learnt from the Development of GIS Application on Azure Cloud Platform

OpenACC Based GPU Parallelization of Plane Sweep Algorithm for Geometric Intersection

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