Large-scale geo-spatial raster selection method based on a user-defined condition using GPGPU - INRAE - Institut national de recherche pour l’agriculture, l’alimentation et l’environnement Access content directly
Conference Papers Year : 2018

Large-scale geo-spatial raster selection method based on a user-defined condition using GPGPU

Méthode de sélection de raster géospatiale à grande échelle basée sur une condition définie par l'utilisateur et à l'aide de GPGPU

Abstract

Spatial database is the cornerstone of any GIS. It is designed for data with spatial attributes. With the tremendous improvement in several technologies for instance: sensors and internet of things, the size of spatial data in general and environmental data in particular is growing quickly. The extraction of relevant information via spatial queries requires searching through this huge volume of data, which is time consuming. Therefore, there is a growing need for better performance. Unfortunately, existing algorithms and methods are based on traditional computing framework (uniprocessors) which makes them incapable to deal with large scale data and as a result we get poor performance. In this work, we report our designs, implementation and experiments of large scale spatial raster selection query based on the modern high performance GPUs. Our aim is to speed up the underlying query execution using parallel primitives and pure CUDA. Our test results show that our methods are faster and good performance is achieved even with large scale rasters and large scale dataset.
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Dates and versions

hal-02608085 , version 1 (16-05-2020)

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Cite

Driss En-Nejjary, François Pinet, Myoung-Ah Kang. Large-scale geo-spatial raster selection method based on a user-defined condition using GPGPU. 11th International Conference of Computer Science and Information Technology ICCSIT 2018, Dec 2018, Paris, France. pp.8. ⟨hal-02608085⟩
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