Understanding the influence of multiple parameters in a complex simulation setting is a difficult task. In the ideal case, the scientist can freely steer such a simulation and is immediately presented with the results for a certain configuration of the input parameters. Such an exploration process is however not possible if the simulation is computationally too expensive. For these cases we present in this paper a scalable computational steering approach utilizing a fast surrogate model as substitute for the time-consuming simulation. The surrogate model we propose is based on the sparse grid technique, and we identify the main computational tasks associated with its evaluation and its extension. We further show how distributed data management combined with the specific use of accelerators allows us to approximate and deliver simulation results to a high-resolution visualization system in real-time. This significantly enhances the steering workflow and facilitates the interactive exploration of large datasets. © 2012 IEEE.
|Original language||English (US)|
|Title of host publication||2012 11th International Symposium on Parallel and Distributed Computing|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||8|
|State||Published - Jun 2012|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): UK-C0020
Acknowledgements: This publication is based on work supported by Award No.UK-C0020, made by King Abdullah University of Science andTechnology (KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.