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dc.contributor.authorEilemann, Stefan
dc.date.accessioned2020-01-14T12:11:03Z
dc.date.available2020-01-14T12:11:03Z
dc.date.issued2019-02-23
dc.identifier.urihttp://hdl.handle.net/10754/661030
dc.description.abstractWe are living in the big data age: An ever increasing amount of data is being produced through data acquisition and computer simulations. While large scale analysis and simulations have received significant attention for cloud and high-performance computing, software to efficiently visualise large data sets is struggling to keep up. Visualization has proven to be an efficient tool for understanding data, in particular visual analysis is a powerful tool to gain intuitive insight into the spatial structure and relations of 3D data sets. Large-scale visualization setups are becoming ever more affordable, and high-resolution tiled display walls are in reach even for small institutions. Virtual reality has arrived in the consumer space, making it accessible to a large audience. This thesis addresses these developments by advancing the field of parallel rendering. We formalise the design of system software for large data visualization through parallel rendering, provide a reference implementation of a parallel rendering framework, introduce novel algorithms to accelerate the rendering of large amounts of data, and validate this research and development with new applications for large data visualization. Applications built using our framework enable domain scientists and large data engineers to better extract meaning from their data, making it feasible to explore more data and enabling the use of high-fidelity visualization installations to see more detail of the data.
dc.description.sponsorshipThe research leading to this proposal was supported in part by the Blue Brain Project, the Swiss National Science Foundation under Grant 200020-129525, the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102 (Human Brain Project), the Hasler Stiftung grant (project number 12097), and the King Abdullah University of Science and Technology (KAUST) through the KAUST-EPFL alliance for Neuro-Inspired High Performance Computing. I would like to take the opportunity to thank the Blue Brain Project and its visualization team, RTT AG (now part of Dassault Systems), KAUST, University of Siegen, the Electronic Visualization Laboratory at the University of Illinois Chicago, and all the other contributors for their support in the research and development leading to this thesis. I would like to thank Prof. Renato Pajarola and the VMML for his longterm commitment to my research work and Patrick Bouchaud for putting me onto the path taken by this thesis. A special gratitude goes to all collaborators who joined me in this endeavour: Daniel Nachbaur, Cedric Stalder, Maxim Makhinya, Christian Marten, Dardo D. Kleiner, Carsten Rohn, Daniel Pfeifer, Sarah Amsellem, Juan Hernando, Marwan Abdellah, Raphael Dumusc, Lucas Peetz Dulley, Jafet Villafranca, Philippe Robert, Ahmet Bilgili, Tobias Wolf, Dustin Wueest,and Martin Lambers.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/1902.08755
dc.rightsArchived with thanks to arXiv
dc.titleParallel Rendering and Large Data Visualization
dc.typePreprint
dc.eprint.versionPre-print
dc.contributor.institutionUniversity of Zurich
dc.identifier.arxivid1902.08755
dc.versionv1
refterms.dateFOA2020-01-14T12:12:28Z
kaust.acknowledged.supportUnitVisualization Laboratory


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