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An efficient data organization and scheduling strategy for accelerating large vector data rendering / Mingqiang Guo in Transactions in GIS, vol 21 n° 6 (December 2017)  

Public [article]  inTransactions in GIS > vol 21 n° 6 (December 2017) . - pp 1217 - 1236 Titre : An efficient data organization and scheduling strategy for accelerating large vector data rendering Type de document : Article/Communication Auteurs : Mingqiang Guo, Auteur ; Ying Huang, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 1217 - 1236 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] données vectorielles [Termes IGN] processeur graphique [Termes IGN] processeur multicoeur [Termes IGN] rendu (géovisualisation) [Termes IGN] traitement parallèle [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Rendering large volumes of vector data is computationally intensive and therefore time consuming, leading to lower efficiency and poorer interactive experience. Graphics processing units (GPUs) are powerful tools in data parallel processing but lie idle most of the time. In this study, we propose an approach to improve the performance of vector data rendering by using the parallel computing capability of many‐core GPUs. Vertex transformation, largely a mathematical calculation that does not require communication with the host storage device, is a time‐consuming procedure because all coordinates of each vector feature need to be transformed to screen vertices. Use of a GPU enables optimization of a general‐purpose mathematical calculation, enabling the procedure to be executed in parallel on a many‐core GPU and optimized effectively. This study mainly focuses on: (1) an organization and storage strategy for vector data based on equal pitch alignment, which can adapt to the GPU's calculating characteristics; (2) a paging‐coalescing transfer and memory access strategy for vector data between the CPU and the GPU; and (3) a balancing allocation strategy to take full advantage of all processing cores of the GPU. Experimental results demonstrate that the approach proposed can significantly improve the efficiency of vector data rendering. Numéro de notice : A2017-837 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12275 Date de publication en ligne : 23/05/2017 En ligne : https://doi.org/10.1111/tgis.12275 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89373 [article]

A simulated annealing algorithm for zoning in planning using parallel computing / Inès Santé in Computers, Environment and Urban Systems, vol 59 (September 2016)  

Public [article]  inComputers, Environment and Urban Systems > vol 59 (September 2016) . - pp 95 - 106 Titre : A simulated annealing algorithm for zoning in planning using parallel computing Type de document : Article/Communication Auteurs : Inès Santé, Auteur ; Francisco F. Rivera, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 95 - 106 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] algorithme du recuit simulé [Termes IGN] Galice (Espagne) [Termes IGN] optimisation (mathématiques) [Termes IGN] parcelle cadastrale [Termes IGN] partition d'image [Termes IGN] polygone [Termes IGN] processeur multicoeur [Termes IGN] utilisation du sol Résumé : (auteur) There is an increasing demand for tools that support land use planning processes, particularly the design of zoning maps, which is one of the most complex tasks in the field. In this task, different land use categories need to be allocated according to multiple criteria. The problem can be formalized in terms of a multiobjective problem. This paper generalizes and complements a previous work on this topic. It presents an algorithm based on a simulated annealing heuristic that optimizes the delimitation of land use categories on a cadastral parcel map according to suitability and compactness criteria. The relative importance of both criteria can be adapted to any particular case. Despite its high computational cost, the use of plot polygons was decided because it is realistic in terms of technical application and land use laws. Due to the computational costs of our proposal, parallel implementations are required, and several approaches for shared memory systems such as multicores are analysed in this paper. Results on a real case study conducted in the Spanish municipality of Guitiriz show that the parallel algorithm based on simulated annealing is a feasible method to design alternative zoning maps. Comparisons with results from experts are reported, and they show a high similarity. Results from our strategy outperform those by experts in terms of suitability and compactness. The parallel version of the code produces good results in terms of speed-up, which is crucial for taking advantage of the architecture of current multicore processors. Numéro de notice : A2016-407 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.compenvurbsys.2016.05.005 Date de publication en ligne : 10/06/2016 En ligne : http://dx.doi.org/10.1016/j.compenvurbsys.2016.05.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81240 [article]

Spaceborne synthetic aperture radar data focusing on multicore-based architectures / Pasquale Imperatore in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 8 (August 2016) . - pp 4712 - 4731 Titre : Spaceborne synthetic aperture radar data focusing on multicore-based architectures Type de document : Article/Communication Auteurs : Pasquale Imperatore, Auteur ; Antonio Pepe, Auteur ; Riccardo Lanari, Auteur Année de publication : 2016 Article en page(s) : pp 4712 - 4731 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] architecture orientée services [Termes IGN] données multisources [Termes IGN] focalisation [Termes IGN] image radar moirée [Termes IGN] implémentation (informatique) [Termes IGN] partage de données localisées [Termes IGN] processeur multicoeur Résumé : (Auteur) This paper describes a general-purpose parallel scheme for efficiently focusing synthetic aperture radar (SAR) data on multicore-based shared-memory architectures. The rationale of the proposed tiling-based parallel focusing model is first discussed, and then, its implementation structure is illustrated. The adopted parallel solution, which is based on a canonical processing pattern, exploits a segmented-block-based approach and works successfully on data acquired by different spaceborne SAR platforms. Insofar as a significant portion of the focusing algorithm is amenable to tiling, our approach decomposes the problem into simpler subproblems of the same type, also providing a suitable mechanism to explicitly control the granularity of computation through the proper specification of the tiling at the different stages of the algorithm itself. Relevant implementation makes use of multithreading and high-performance libraries. Achievable performances are then experimentally investigated by quantifying the benefit of the parallelism incorporated into the prototype solution, thus demonstrating the validity of our approach. Accordingly, canonical performance metrics have been evaluated, and the pertinent scalability has been examined on different multicore architectures. Furthermore, in order to emphasize the practical ability of the proposed parallel model implementation to efficiently deal with data of different SAR sensors, a performance analysis has been carried out in different realistic scenarios including data acquired by the Envisat/ASAR, RADARSAT-1, and COSMO-SkyMed platforms. Numéro de notice : A2016-889 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2550201 En ligne : https://doi.org/10.1109/TGRS.2016.2550201 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83069 [article]

Parallel performance of typical algorithms in remote sensing-based mapping on a multi-core computer / Jinghui Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 5 (May 2015)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 81 n° 5 (May 2015) . - pp 373 - 385 Titre : Parallel performance of typical algorithms in remote sensing-based mapping on a multi-core computer Type de document : Article/Communication Auteurs : Jinghui Yang, Auteur ; Jixian Zhang, Auteur Année de publication : 2015 Article en page(s) : pp 373 - 385 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] processeur multicoeur [Termes IGN] traitement d'image [Termes IGN] traitement parallèle Résumé : (auteur) Typical algorithms in remote sensing-based mapping, such as geometric correction, image fusion, image mosaic, and automatic DEM extractions, are data- and computation-intensive; processing on multi-core computers can improve their performance. Therefore, parallel computing methods that can fully leverage state-of-the-art hardware platforms and that can be easily adapted to these algorithms are required. In this paper, a method with high parallelism is adopted. The method integrates a recursive procedure with a parallel mechanism that is capable of concurrently processing multiple blocks on multiple cores. The parallel experiments of five categories of typical algorithms on two multi-core computers with Windows and Linux operating systems, respectively, were fulfilled. The experimental results show that although the gains of parallel performance vary for different algorithms, the processing performance achieved on multi-core computers is significantly improved. The best case on a computer with two CPUs is able to perform the DEM extractions up to 13.6 times faster than serial execution. According to these experiments, the factors influencing parallel performance on a multi-core computer are discussed. Numéro de notice : A2015-972 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.14358/PERS.81.5.373 En ligne : https://doi.org/10.14358/PERS.81.5.373 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80042 [article]

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