By Janine Bennett, Fabien Vivodtzev, Valerio Pascucci
This ebook includes papers offered on the Workshop at the research of Large-scale, High-Dimensional, and Multi-Variate info utilizing Topology and records, held in Le Barp, France, June 2013. It beneficial properties the paintings of a few of the main sought after and well-known leaders within the box who research demanding situations in addition to aspect recommendations to the research of maximum scale data.
The booklet offers new tools that leverage the mutual strengths of either topological and statistical innovations to aid the administration, research, and visualization of advanced facts. It covers either idea and alertness and gives readers with an summary of significant key ideas and the newest study trends.
Coverage within the publication contains multi-variate and/or high-dimensional research suggestions, feature-based statistical tools, combinatorial algorithms, scalable records algorithms, scalar and vector box topology, and multi-scale representations. additionally, the e-book info algorithms which are greatly acceptable and will be utilized by program scientists to glean perception from quite a lot of complicated info sets.
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Additional resources for Topological and Statistical Methods for Complex Data: Tackling Large-Scale, High-Dimensional, and Multivariate Data Spaces
It differs from EPSN in design by having lower requirements as it only needs the use of a single HDF5 library and file driver extension. Such solutions offer tools for researchers to interact with their simulations by allowing them, not only to monitor their current states but also to modify the parameters of the remaining simulation steps. Those computational steering solutions as well as the RealityGrid project  focus on interactivity with simulation whereas our main objective is to provide in-situ visualization operations to researchers while minimizing I/O overhead and disk space use.
In the past, studies and improvements in scientific simulation have been mainly focused on the solver, due to being the most cycle-consuming part in the simulation process. Thus, visualization has been traditionally run sequentially on a smaller computer and at the very end of the solver computation. At the time, this was easily explained by the small need for both memory and computation resources in most of the visualization cases. Nevertheless, with the increase of our computational capabilities, we tend to use and generate much more data than what we were used to.
In: Proceedings of the IEEE 2001 Symposium on Parallel and Large-Data Visualization and Graphics, pp. 125–159 (2001) 10. : In-situ visualization in fluid mechanics using catalyst: a case study for code saturne. In: IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV), pp. 53–57, (2013) 11. : Design issues for performing in situ analysis of simulation data. Technical Report SAND2009-2014, Sandia National Laboratories, pp. 7–18 (2009) 12. : Salome platform component model for numerical simulation.
Topological and Statistical Methods for Complex Data: Tackling Large-Scale, High-Dimensional, and Multivariate Data Spaces by Janine Bennett, Fabien Vivodtzev, Valerio Pascucci