Implementation of a parallel high-performance visualization technique in GRASS GIS

Sorokine, Alexandre

doi:10.1016/j.cageo.2006.09.008

Cited by 28 publications

(13 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Typical efforts can be demonstrated in spatial interpolation [26][27][28], spatial statistics [26,[29][30][31], spatial visualization [32], viewshed analysis [33,34], spatial simulation [35,36], geocollaboration [27,37], etc. In most cases, the optimization strategy used in one algorithm may not work for another.…”

Section: Computationally Intensive Geocomputationmentioning

confidence: 99%

Graph-Based Divide and Conquer Method for Parallelizing Spatial Operations on Vector Data

Kang

Lin

2014

Remote Sensing

View full text Add to dashboard Cite

Abstract:In computer science, dependence analysis determines whether or not it is safe to parallelize statements in programs. In dealing with the data-intensive and computationally intensive spatial operations in processing massive volumes of geometric features, this dependence can be well utilized for exploiting the parallelism. In this paper, we propose a graph-based divide and conquer method for parallelizing spatial operations (GDCMPSO) on vector data. It can represent spatial data dependences in spatial operations through representing the vector features as graph vertices, and their computational dependences as graph edges. By this way, spatial operations can be parallelized in three steps: partitioning the graph into graph components with inter-component edges firstly, simultaneously processing multiple subtasks indicated by the graph components secondly and finally handling remainder tasks denoted by the inter-component edges. To demonstrate how it works, buffer operation and intersection operation under this paradigm are conducted. In a 12-core environment, the two spatial operations both gain obvious performance improvements, and the speedups are more than eight. The testing results suggest that GDCMPSO contributes to a method for parallelizing spatial operations and can greatly improve the computing efficiency on multi-core architectures.

show abstract

Section: Computationally Intensive Geocomputationmentioning

confidence: 99%

Graph-Based Divide and Conquer Method for Parallelizing Spatial Operations on Vector Data

Kang

Lin

2014

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Because of its flexibility, GRASS has been customised for very many different platforms; Sorokine (2007) shows how parallel high-performance visualization may be made available for tiled wall displays. Rocchini et al (2011) customise GRASS to rectify aerial photographs as a basis for constructing landscape composition indices for tracking climate change.…”

Section: Desktop Applicationsmentioning

confidence: 99%

Geocomputation and Open Source Software: Components and Software Stacks

Bivand

2011

SSRN Journal

View full text Add to dashboard Cite

Geocomputation, with its necessary focus on software development and methods innovation, has enjoyed a close relationship with free and open source software communities. These extend from communities providing the numerical infrastructure for computation, such as BLAS (Basic Linear Algebra Subprograms), through language communities around Python, Java and others, to communities supporting spatial data handling, especially the projects of the Open Source Geospatial Foundation. This chapter surveys the stack of software components available for geocomputation from these sources, looking in most detail at the R language and environment, and how OSGeo projects have been interfaced with it. In addition, attention will be paid to open development models and community participation in software development. Since free and open source geospatial software has also achieved a successively greater presence in proprietary software as computational platforms evolve, the chapter will close with some indications of future trends in software component stacks, using Terralib as an example.

show abstract

“…The majority of research on OWS-based distributed geospatial processing has focused on seamless data sharing, interoperability, and service chaining. With the increasing use of geospatial applications and the expanding volume of geospatial data, performance issues of the OWS service have attracted considerable attention in recent studies [6,7]. However, constructing a high-performance WPS on a physical cluster via parallel computing technology means that the WPS will occupy the entire cluster.…”

Section: Introductionmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Implementation of a parallel high-performance visualization technique in GRASS GIS

Cited by 28 publications

References 6 publications

Graph-Based Divide and Conquer Method for Parallelizing Spatial Operations on Vector Data

Graph-Based Divide and Conquer Method for Parallelizing Spatial Operations on Vector Data

Geocomputation and Open Source Software: Components and Software Stacks

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

Contact Info

Product

Resources

About