Explorations of the implementation of a parallel IDW interpolation algorithm in a Linux cluster-based parallel GIS

Huang, Fang; Liu, Dingsheng; Tan, Xicheng; Wang, Jian; Chen, Yunping; He, Baina

doi:10.1016/j.cageo.2010.05.024

Cited by 87 publications

(48 citation statements)

References 12 publications

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“…Parallel computing divides a task into multiple sub-tasks that can be executed within a number of processes upon several processors [25]; in this manner, large problems can be solved efficiently by using massively parallel processor (MPP) systems [26]. Parallel computing technologies have been recommended in the geosciences.…”

Section: Parallel Computingmentioning

confidence: 99%

“…Various techniques for the high-performance parallel processing and analysis of remote sensing data and geospatial datasets based on clusters have been proposed [27][28][29]. Geospatial applications based on parallel computing have also been designed and implemented [25,30]. Parallel algorithms based on GPUs have also received considerable attention as GPUs are more efficient than CPUs [31].…”

Section: Parallel Computingmentioning

confidence: 99%

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Building an Elastic Parallel OGC Web Processing Service on a Cloud-Based Cluster: A Case Study of Remote Sensing Data Processing Service

Tan

Deng

et al. 2015

Sustainability

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Since the Open Geospatial Consortium (OGC) proposed the geospatial Web Processing Service (WPS), standard OGC Web Service (OWS)-based geospatial processing has become the major type of distributed geospatial application. However, improving the performance and sustainability of the distributed geospatial applications has become the dominant challenge for OWSs. This paper presents the construction of an elastic parallel OGC WPS service on a cloud-based cluster and the designs of a high-performance, cloud-based WPS service architecture, the scalability scheme of the cloud, and the algorithm of the elastic parallel geoprocessing. Experiments of the remote sensing data processing service demonstrate that our proposed method can provide a higher-performance WPS service OPEN ACCESSSustainability 2015, 7 14246 that uses less computing resources. Our proposed method can also help institutions reduce hardware costs, raise the rate of hardware usage, and conserve energy, which is important in building green and sustainable geospatial services or applications.

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Section: Parallel Computingmentioning

confidence: 99%

Section: Parallel Computingmentioning

confidence: 99%

Building an Elastic Parallel OGC Web Processing Service on a Cloud-Based Cluster: A Case Study of Remote Sensing Data Processing Service

Tan

Deng

et al. 2015

Sustainability

Self Cite

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“…When the interpolation results are ready, this module also writes the data as the output. These functions are implemented using an open-source spatial data format conversion library, GDAL (Geospatial Data Abstraction Library) [16]. APSM focuses mainly on calculating the plane coordinates px, yq of the unknown points according to the coordinate range of the known points and using a fixed step.…”

Section: Implementation Of the Serial Algorithmmentioning

confidence: 99%

OpenCL Implementation of a Parallel Universal Kriging Algorithm for Massive Spatial Data Interpolation on Heterogeneous Systems

Huang

Jian

et al. 2016

IJGI

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Abstract:In some digital Earth engineering applications, spatial interpolation algorithms are required to process and analyze large amounts of data. Due to its powerful computing capacity, heterogeneous computing has been used in many applications for data processing in various fields. In this study, we explore the design and implementation of a parallel universal kriging spatial interpolation algorithm using the OpenCL programming model on heterogeneous computing platforms for massive Geo-spatial data processing. This study focuses primarily on transforming the hotspots in serial algorithms, i.e., the universal kriging interpolation function, into the corresponding kernel function in OpenCL. We also employ parallelization and optimization techniques in our implementation to improve the code performance. Finally, based on the results of experiments performed on two different high performance heterogeneous platforms, i.e., an NVIDIA graphics processing unit system and an Intel Xeon Phi system (MIC), we show that the parallel universal kriging algorithm can achieve the highest speedup of up to 40ˆwith a single computing device and the highest speedup of up to 80ˆwith multiple devices.

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“…Huang [10] proposed a grid approach to spatial data management based purely in databases technologies and distributed processing of queries. More recently, Huang [11] presented a native parallel implementation of GRASS GIS software [12]. This development enables users to attain high performance capabilities for their operations, but is restricted to that specific GIS suite.…”

Section: Introductionmentioning

confidence: 99%

Performance enhancement of a GIS-based facility location problem using desktop grid infrastructure

et al. 2013

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Alfonso Laguna, CD.; Hernández García, V. (2013). Performance enhancement of a GIS-based facility location problem using desktop grid infrastructure. Earth Science Informatics. 6(4):199-207. doi:10.1007/s12145-013-0119-1.Performance enhancement of a GIS-based facility location problem using desktop grid infrastructure Andrés García-García (a) , Carolina Perpiñá consisting of a set of the experiments on the case study, concludes that the desktop grid infrastructure is able to use a commercial GIS system to solve the spatial problem achieving high speedup and computational resource utilization. Particularly, the results of the experiments showed an increase in speedup of fourteen times using sixteen computers and a computational efficiency greater than 87% compared with the sequential procedure.

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Explorations of the implementation of a parallel IDW interpolation algorithm in a Linux cluster-based parallel GIS

Cited by 87 publications

References 12 publications

Building an Elastic Parallel OGC Web Processing Service on a Cloud-Based Cluster: A Case Study of Remote Sensing Data Processing Service

Building an Elastic Parallel OGC Web Processing Service on a Cloud-Based Cluster: A Case Study of Remote Sensing Data Processing Service

OpenCL Implementation of a Parallel Universal Kriging Algorithm for Massive Spatial Data Interpolation on Heterogeneous Systems

Performance enhancement of a GIS-based facility location problem using desktop grid infrastructure

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