Descripteur
Termes IGN > sciences naturelles > physique > électronique > composant électronique > processeur > processeur graphique
processeur graphique |
Documents disponibles dans cette catégorie (19)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
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)
[article]
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 IGN] données massives
[Termes IGN] estimation par noyau
[Termes IGN] jeu de données localisées
[Termes IGN] optimisation (mathématiques)
[Termes IGN] processeur graphiqueRésumé : (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 Affiliation des auteurs : non IGN 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
in International journal of geographical information science IJGIS > vol 31 n° 9-10 (September - October 2017) . - pp 2068 - 2097[article]Réservation
Réserver ce documentExemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité 079-2017051 RAB Revue Centre de documentation En réserve L003 Disponible 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)
[article]
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 IGN] analyse comparative
[Termes IGN] compensation par faisceaux
[Termes IGN] jeu de données
[Termes IGN] méthode du gradient conjugué
[Termes IGN] processeur graphique
[Termes IGN] traitement parallèleRésumé : (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 Affiliation des auteurs : non IGN 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
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 9 (September 2017) . - pp 633 - 641[article]A parallel scheme for large-scale polygon rasterization on CUDA-enabled GPUs / Chen Zhou in Transactions in GIS, vol 21 n° 3 (June 2017)
[article]
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 IGN] données massives
[Termes IGN] maillage
[Termes IGN] polygone
[Termes IGN] processeur
[Termes IGN] processeur graphique
[Termes IGN] rastérisation
[Termes IGN] temps
[Termes IGN] traitement parallèleRésumé : (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 Affiliation des auteurs : non IGN 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
in Transactions in GIS > vol 21 n° 3 (June 2017) . - pp 608 – 631[article]The index array approach and the dual tiled similarity algorithm for UAS hyper-spatial image processing / Lihong Su in Geoinformatica, vol 20 n° 4 (October - December 2016)
[article]
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 IGN] distorsion d'image
[Termes IGN] géoréférencement
[Termes IGN] processeur graphiqueRésumé : (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 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-016-0253-2 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
in Geoinformatica > vol 20 n° 4 (October - December 2016) . - pp 859 - 878[article]Efficient visualization of urban simulation data using modern GPUs / Aleksandr Zagarskikh in Procedia Computer Science, vol 51 (2015)
[article]
Titre : Efficient visualization of urban simulation data using modern GPUs Type de document : Article/Communication Auteurs : Aleksandr Zagarskikh, Auteur ; Andrey Karsakov, Auteur ; Alexey Bezgodov, Auteur Année de publication : 2015 Conférence : ICCS 2015, International Conference on Computational Science, Computational Science at the Gates of Nature 01/06/2015 03/06/2015 Reykjavík Islande Article en page(s) : pp 2928 - 2932 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Informatique
[Termes IGN] base de données urbaines
[Termes IGN] processeur graphique
[Termes IGN] simulation 3D
[Termes IGN] visualisation de donnéesRésumé : (auteur) Visualization of simulation results in major urban areas is a difficult task. Multi-scale processes and connectivity of the urban environment may require interactive visualization of dynamic scenes with lots of objects at different scales. To visualize these scenes it is not always possible to use standard GIS systems. Wide distribution of high-performance gaming graphics cards has led to the emergence of specialized frameworks, which are able to cope with such kinds of visualization. This paper presents a framework and special algorithms that take full advantage of the GPU to render the urban simulation data over a virtual globe. The experiments on a scalability of the framework have showed that the framework is successfully deals with the visualization of up to two million moving agents and up to eight million of fixed points of interest on top of the virtual globe without detriment to smoothness of the image. Numéro de notice : A2015--086 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1016/j.procs.2015.05.481 En ligne : https://doi.org/10.1016/j.procs.2015.05.481 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84753
in Procedia Computer Science > vol 51 (2015) . - pp 2928 - 2932[article]Out-of-core GPU-based change detection in massive 3D point clouds / Rico Richter in Transactions in GIS, vol 17 n° 5 (October 2013)PermalinkVisualisation 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)PermalinkPermalink