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Big Data 2019, IEEE International Conference on Big Data 09/12/2019 12/12/2019 Los Angeles Californie - Etats-Unis Proceedings IEEE
nom du congrès :
Big Data 2019, IEEE International Conference on Big Data
début du congrès :
09/12/2019
fin du congrès :
12/12/2019
ville du congrès :
Los Angeles
pays du congrès :
Californie - Etats-Unis
site des actes du congrès :
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Documents disponibles (1)
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Titre : Tile & merge: Distributed Delaunay triangulations for cloud computing Type de document : Article/Communication Auteurs : Laurent Caraffa , Auteur ; Pooran Memari, Auteur ; Murat Yirci, Auteur ; Mathieu Brédif
Editeur : New-York : IEEE Computer society Année de publication : 2019 Projets : 1-Pas de projet / Conférence : Big Data 2019, IEEE International Conference on Big Data 09/12/2019 12/12/2019 Los Angeles Californie - Etats-Unis Proceedings IEEE Importance : 7 p. Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] géomètrie algorithmique
[Termes IGN] informatique en nuage
[Termes IGN] jeu de données
[Termes IGN] mémoire d'ordinateur
[Termes IGN] reconstruction d'objet
[Termes IGN] semis de points
[Termes IGN] Spark
[Termes IGN] triangulation de DelaunayRésumé : (auteur) Motivated by the needs of a scalable out-of-core surface reconstruction algorithm available on the cloud, this paper addresses the computation of distributed Delaunay triangulations of massive point sets. The proposed algorithm takes as input a point cloud and first partitions it across multiple processing elements into tiles of relatively homogeneous point sizes. The distributed computation and communication between processing
elements is orchestrated so that each one discovers the Delaunay neighbors of its input points within the theoretical overall Delaunay triangulation of all points and computes locally a partial view of this triangulation. This approach prevents memory limitations
by never materializing the global triangulation. This efficiency is due to our proposed uncentralized model to represent, manage and locally construct the triangulation corresponding to each tile. The point set is first partitioned into non-overlapping tiles, then we construct within each tile the Delaunay triangulation of the local points and a minimal set of replicated foreign points in order to capture the simplices spanning multiple tiles. Inspired by the star splaying approach for Delaunay triangulation computation/repair, communication is limited to exchanging points of potential Delaunay neighbors across tiles. Therefore, our method is guaranteed to reconstruct, within each tile, a triangulation that contains the star of its local points, as though it were computed within the Delaunay triangulation of all points. The proposed algorithm is implemented with Spark for the scheduling and C++ for the geometric computations. This allows both an optimal scheduling on multiple machines and efficient low-level computation. The results show the efficiency of our algorithm in terms of speedup and strong scaling on a classical Spark configuration with both synthetic and real use case
datasets.Numéro de notice : C2019-033 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE/MATHEMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/BigData47090.2019.9006534 Date de publication en ligne : 24/02/2020 En ligne : https://doi.org/10.1109/BigData47090.2019.9006534 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95318
