abstract: Nearly 25 years ago, parallel computing techniques were first applied to vector spatial analysis methods. This initial research was driven by the desire to reduce computing times in order to support scaling to larger problem sets. Since this initial work, rapid technological advancement has driven the availability of High Performance Computing (HPC) resources, in the form of multi-core desktop computers, distributed geographic information processing systems, e.g. computational grids, and single site HPC clusters. In step with increases in computational resources, significant advancement in the capabilities to capture and store large quantities of spatially enabled data have been realized. A key component to utilizing vast data qua...
Abstract: The object of this article is the parallelization of kriging, which is an estimation metho...
ii Vector Spatial data types such as lines, polygons or regions etc usually comprises of hundreds of...
Spatial-temporal modelling of environmental systems such as agriculture, forestry, and water resourc...
Computing tasks may be parallelized top-down by splitting into per-node chunks when the tasks permit...
Geo-Spatial computing and data analysis is the branch of computer science that deals with real world...
Most parallel processing methods developed for geographic analyses bind the design of domain decompo...
Geographic information systems (GIS) are performing increasingly sophisticated analyses on growing d...
International audienceIn this paper, we show that various concepts and tools developed in the 90's i...
Rapidly growing volume of spatial data has made it desirable to develop efficient techniques for man...
Spatial association measures, when computed for large data sets, have significant computational requ...
Spatial optimization (SO) is an important and prolific field of interdisciplinary research. Spatial ...
Large datasets require efficient processing, storage and management to efficiently extract useful in...
As massive data sets become increasingly available, people are facing the problem of how to effectiv...
In computer science, dependence analysis determines whether or not it is safe to parallelize stateme...
Copyright © 2003 Published by Elsevier Science B.V.The number of applications that require parallel ...
Abstract: The object of this article is the parallelization of kriging, which is an estimation metho...
ii Vector Spatial data types such as lines, polygons or regions etc usually comprises of hundreds of...
Spatial-temporal modelling of environmental systems such as agriculture, forestry, and water resourc...
Computing tasks may be parallelized top-down by splitting into per-node chunks when the tasks permit...
Geo-Spatial computing and data analysis is the branch of computer science that deals with real world...
Most parallel processing methods developed for geographic analyses bind the design of domain decompo...
Geographic information systems (GIS) are performing increasingly sophisticated analyses on growing d...
International audienceIn this paper, we show that various concepts and tools developed in the 90's i...
Rapidly growing volume of spatial data has made it desirable to develop efficient techniques for man...
Spatial association measures, when computed for large data sets, have significant computational requ...
Spatial optimization (SO) is an important and prolific field of interdisciplinary research. Spatial ...
Large datasets require efficient processing, storage and management to efficiently extract useful in...
As massive data sets become increasingly available, people are facing the problem of how to effectiv...
In computer science, dependence analysis determines whether or not it is safe to parallelize stateme...
Copyright © 2003 Published by Elsevier Science B.V.The number of applications that require parallel ...
Abstract: The object of this article is the parallelization of kriging, which is an estimation metho...
ii Vector Spatial data types such as lines, polygons or regions etc usually comprises of hundreds of...
Spatial-temporal modelling of environmental systems such as agriculture, forestry, and water resourc...