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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 descripteurs IGN] données vectorielles [Termes descripteurs IGN] processeur graphique [Termes descripteurs IGN] processeur multicoeur [Termes descripteurs IGN] rendu (géovisualisation) [Termes descripteurs 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 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 GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data / Guiming Zhang in International journal of geographical information science IJGIS, vol 31 n° 9-10 (September - October 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 9-10 (September - October 2017) . - pp 2068 - 2097 Titre : A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data Type de document : Article/Communication Auteurs : Guiming Zhang, Auteur ; A - Xing Zhu, Auteur ; Qunying Huang, Auteur Année de publication : 2017 Article en page(s) : pp 2068 - 2097 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] données massives [Termes descripteurs IGN] estimation par noyau [Termes descripteurs IGN] jeu de données localisées [Termes descripteurs IGN] optimisation (mathématiques) [Termes descripteurs IGN] processeur graphique Résumé : (Auteur) Kernel density estimation (KDE) is a classic approach for spatial point pattern analysis. In many applications, KDE with spatially adaptive bandwidths (adaptive KDE) is preferred over KDE with an invariant bandwidth (fixed KDE). However, bandwidths determination for adaptive KDE is extremely computationally intensive, particularly for point pattern analysis tasks of large problem sizes. This computational challenge impedes the application of adaptive KDE to analyze large point data sets, which are common in this big data era. This article presents a graphics processing units (GPUs)-accelerated adaptive KDE algorithm for efficient spatial point pattern analysis on spatial big data. First, optimizations were designed to reduce the algorithmic complexity of the bandwidth determination algorithm for adaptive KDE. The massively parallel computing resources on GPU were then exploited to further speed up the optimized algorithm. Experimental results demonstrated that the proposed optimizations effectively improved the performance by a factor of tens. Compared to the sequential algorithm and an Open Multiprocessing (OpenMP)-based algorithm leveraging multiple central processing unit cores for adaptive KDE, the GPU-enabled algorithm accelerated point pattern analysis tasks by a factor of hundreds and tens, respectively. Additionally, the GPU-accelerated adaptive KDE algorithm scales reasonably well while increasing the size of data sets. Given the significant acceleration brought by the GPU-enabled adaptive KDE algorithm, point pattern analysis with the adaptive KDE approach on large point data sets can be performed efficiently. Point pattern analysis on spatial big data, computationally prohibitive with the sequential algorithm, can be conducted routinely with the GPU-accelerated algorithm. The GPU-accelerated adaptive KDE approach contributes to the geospatial computational toolbox that facilitates geographic knowledge discovery from spatial big data. Numéro de notice : A2017-509 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2017.1324975 En ligne : http://dx.doi.org/10.1080/13658816.2017.1324975 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86455 [article]

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A new GPU bundle adjustment method for large-scale data / Zhou Shunping ; Xiong Xiaodong ; Junfeng Zhu in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 9 (September 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 9 (September 2017) . - pp 633 - 641 Titre : A new GPU bundle adjustment method for large-scale data Type de document : Article/Communication Auteurs : Zhou Shunping, Auteur ; Xiong Xiaodong, Auteur ; Junfeng Zhu, Auteur Année de publication : 2017 Article en page(s) : pp 633 - 641 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] compensation par faisceaux [Termes descripteurs IGN] gradient conjugué (algorithme) [Termes descripteurs IGN] jeu de données [Termes descripteurs IGN] processeur graphique [Termes descripteurs IGN] traitement parallèle Résumé : (Auteur) We developed a fast and effective bundle adjustment method for large-scale datasets. The preconditioned conjugate gradient (PCG) algorithm and GPU parallel computing technology are simultaneously applied to deal with large-scale data and to accelerate the bundle adjustment process. The whole bundle adjustment process is modified to enable parallel computing. The critical optimization on parallel task assignment and GPU memory usage are specified. The proposed method was tested using 10 datasets. The traditional Levenberg Marquardt (LM) method, advanced PCG method, Wu's method and the proposed GPU parallel computing method are all compared and analyzed. Preliminary results have shown that the proposed method can process a large-scale dataset with about 13,000 images in less than three minutes on a common computer with GPU device. The efficiency of the proposed method is about the same with Wu's method while the accuracy is better. Numéro de notice : A2017-609 Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.9.633 En ligne : https://doi.org/10.14358/PERS.83.9.633 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86887 [article]

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A parallel scheme for large-scale polygon rasterization on CUDA-enabled GPUs / Chen Zhou in Transactions in GIS, vol 21 n° 3 (June 2017)  

Public [article]  inTransactions in GIS > vol 21 n° 3 (June 2017) . - pp 608 – 631 Titre : A parallel scheme for large-scale polygon rasterization on CUDA-enabled GPUs Type de document : Article/Communication Auteurs : Chen Zhou, Auteur ; Zhenjie Chen, Auteur ; Yuzhe Pian, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 608 – 631 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes descripteurs IGN] données massives [Termes descripteurs IGN] maillage [Termes descripteurs IGN] polygone [Termes descripteurs IGN] processeur [Termes descripteurs IGN] processeur graphique [Termes descripteurs IGN] rastérisation [Termes descripteurs IGN] temps [Termes descripteurs IGN] traitement parallèle Résumé : (Auteur) This research develops a parallel scheme to adopt multiple graphics processing units (GPUs) to accelerate large-scale polygon rasterization. Three new parallel strategies are proposed. First, a decomposition strategy considering the calculation complexity of polygons and limited GPU memory is developed to achieve balanced workloads among multiple GPUs. Second, a parallel CPU/GPU scheduling strategy is proposed to conceal the data read/write times. The CPU is engaged with data reads/writes while the GPU rasterizes the polygons in parallel. This strategy can save considerable time spent in reading and writing, further improving the parallel efficiency. Third, a strategy for utilizing the GPU's internal memory and cache is proposed to reduce the time required to access the data. The parallel boundary algebra filling (BAF) algorithm is implemented using the programming models of compute unified device architecture (CUDA), message passing interface (MPI), and open multi-processing (OpenMP). Experimental results confirm that the implemented parallel algorithm delivers apparent acceleration when a massive dataset is addressed (50.32 GB with approximately 1.3 × 108 polygons), reducing conversion time from 25.43 to 0.69 h, and obtaining a speedup ratio of 36.91. The proposed parallel strategies outperform the conventional method and can be effectively extended to a CPU-based environment. Numéro de notice : A2017-626 Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12213 En ligne : http://dx.doi.org/10.1111/tgis.12213 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86941 [article]

The index array approach and the dual tiled similarity algorithm for UAS hyper-spatial image processing / Lihong Su in Geoinformatica [en ligne], vol 20 n° 4 (October - December 2016)  

Public [article]  inGeoinformatica [en ligne] > vol 20 n° 4 (October - December 2016) . - pp 859 - 878 Titre : The index array approach and the dual tiled similarity algorithm for UAS hyper-spatial image processing Type de document : Article/Communication Auteurs : Lihong Su, Auteur ; Y. Huang, Auteur ; James Gibeaut, Auteur ; Longzhuang Li, Auteur Année de publication : 2016 Article en page(s) : pp 859 - 878 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] distorsion d'image [Termes descripteurs IGN] géoréférencement [Termes descripteurs IGN] processeur graphique Résumé : (Auteur) Unmanned aerial systems (UAS) have been used as a robust tool for agricultural and environmental applications in recent years. Remote sensing systems based on UAS typically acquire massive hyper-spatial images in its short turnaround. This paper takes advantage of graphics processing unit (GPU) massive parallel computation in order to process the huge data timely and efficiently. More specifically, this paper presents an index array approach for lens distortion correction and geo-referencing. They are the two essential components in UAS hyper-spatial image processing. The index array approach is also capable of parallelizing image file I/O and the orthoimage generation. In addition, this paper presents the dual tiled similarity algorithm for the image co-registration. The index array approach and the dual tiled similarity algorithm were evaluated using two UAS remote sensing datasets of South Padre island shorelines. The results show that this index array approach was able to speed up at least 10 times the lens distortion correction and the geo-referencing relative to the central processing unit (CPU) computation. This dual tiled algorithm could provide 12 times speedup compared with the CPU similarity computation. Numéro de notice : A2016-817 Thématique : IMAGERIE Nature : Article En ligne : http://dx.doi.org/10.1007/s10707-016-0253-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82621 [article]

Efficient visualization of urban simulation data using modern GPUs / Aleksandr Zagarskikh in Procedia Computer Science, vol 51 (2015)  

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Out-of-core GPU-based change detection in massive 3D point clouds / Rico Richter in Transactions in GIS, vol 17 n° 5 (October 2013)  

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Visualisation 3D de terrain texturé : préservation au niveau du pixel des qualités géométriques et colorimétriques, une méthode temps réel, innovante et simple / T.V. Lê in Revue internationale de géomatique, vol 22 n° 3 (septembre - novembre 2012)

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Fast and accurate visibility computation in urban scenes / Bruno Vallet (2011)    

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