Remote large data visualization in the paraview framework

  • Authors:
  • Andy Cedilnik;Berk Geveci;Kenneth Moreland;James Ahrens;Jean Favre

  • Affiliations:
  • Kitware Inc., New York;Kitware Inc., New York;Sandia National Laboratories, Albuquerque, New Mexico;Los Alamos National Laboratory, Los Alamos, New Mexico;Swiss National Supercomputing Centre, Switzerland

  • Venue:
  • EG PGV'06 Proceedings of the 6th Eurographics conference on Parallel Graphics and Visualization
  • Year:
  • 2006

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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.