The distributed virtual windtunnel
Proceedings of the 1992 ACM/IEEE conference on Supercomputing
Proceedings of the 7th conference on Visualization '96
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
Using MPI (2nd ed.): portable parallel programming with the message-passing interface
High performance visualization of time-varying volume data over a wide-area network status
Proceedings of the 2000 ACM/IEEE conference on Supercomputing
Immersive VR for Scientific Visualization: A Progress Report
IEEE Computer Graphics and Applications
EGVE '03 Proceedings of the workshop on Virtual environments 2003
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
Interactive exploration of large data in hybrid visualization environments
EGVE'07 Proceedings of the 13th Eurographics conference on Virtual Environments
Dynamic regions of interest for interactive flow exploration
EG PGV'07 Proceedings of the 7th Eurographics conference on Parallel Graphics and Visualization
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Explorative analysis of unsteady computational fluid dynamics (CFD) simulations requires a fast extraction of flow features. For time-varying data, the extraction algorithm has to be executed for each time step in the period under observation. Even when parallelised on a remote high performance computer, the user's waiting time still exceeds interactivity criteria for large data sets. Moreover, computations are generally performed in a fixed order, not taking into account the importance of partial results for the user's investigation. In this paper we propose a general method to guide parallel feature extraction on unsteady data sets in order to assist the user during the explorative analysis even though interactive response times might not be available. By re-ordering of single time step computations, the order in which features are provided is arranged according to the user's exploration process. We describe three different concepts based on typical user behaviours. Using this approach, parallel extraction of unsteady features is enhanced for arbitrary extraction methods.