Remote Large Data Visualization in the ParaView Framework
dc.contributor.author | Cedilnik, Andy | en_US |
dc.contributor.author | Geveci, Berk | en_US |
dc.contributor.author | Moreland, Kenneth | en_US |
dc.contributor.author | Ahrens, James | en_US |
dc.contributor.author | Favre, Jean | en_US |
dc.contributor.editor | Alan Heirich and Bruno Raffin and Luis Paulo dos Santos | en_US |
dc.date.accessioned | 2014-01-26T16:30:50Z | |
dc.date.available | 2014-01-26T16:30:50Z | |
dc.date.issued | 2006 | en_US |
dc.description.abstract | Scientists are using remote parallel computing resources to run scientific simulations to model a range of scientific problems. Visualization tools are used to understand the massive datasets that result from these simulations. A number of problems need to be overcome in order to create a visualization tool that effectively visualizes these datasets under this scenario. Problems include how to effectively process and display massive datasets and how to effectively communicate data and control information between the geographically distributed computing and visualization resources. We believe a solution that incorporates a data parallel data server, a data parallel rendering server and client controller is key. Using this data server, render server, client model as a basis, this paper describes in detail a set of integrated solutions to remote/distributed visualization problems including presenting an efficient M to N parallel algorithm for transferring geometry data, an effective server interface abstraction and parallel rendering techniques for a range of rendering modalities including tiled display walls and CAVEs. | en_US |
dc.description.seriesinformation | Eurographics Symposium on Parallel Graphics and Visualization | en_US |
dc.identifier.isbn | 3-905673-40-1 | en_US |
dc.identifier.issn | 1727-348X | en_US |
dc.identifier.uri | https://doi.org/10.2312/EGPGV/EGPGV06/163-170 | en_US |
dc.publisher | The Eurographics Association | en_US |
dc.subject | Categories and Subject Descriptors (according to ACM CCS): I.3.1 [Computer Graphics]: Parallel Processing | en_US |
dc.title | Remote Large Data Visualization in the ParaView Framework | en_US |
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