DocumentCode :
1528416
Title :
Diving deep: data-management and visualization strategies for adaptive mesh refinement simulations
Author :
Norman, Michael L. ; Shalf, John ; Levy, Stuart ; Daues, Greg
Author_Institution :
Nat. Center for Supercomput. Applications, Urbana, IL, USA
Volume :
1
Issue :
4
fYear :
1999
Firstpage :
36
Lastpage :
47
Abstract :
The authors´ cosmological applications illustrate problems and solutions in storing, handling, visualizing, virtually navigating, and remote serving data produced by large scale adaptive mesh refinement simulations. The authors describe their cosmological AMR algorithm and how they applied it to star, galaxy, and galaxy cluster formation. Basically, the algorithm allows them to place very high resolution grids precisely where they are needed-where stars and galaxies condense out of diffuse gas. In these applications, AMR allows the authors to achieve a local mesh refinement, relative to the global coarse grid, of more than a factor of 106. Such resolution would be totally impossible to achieve with a global, uniform fine grid. Thus, AMR allows them to simulate multiscale phenomena that are out of reach with fixed grid methods
Keywords :
astronomy computing; cosmology; data visualisation; digital simulation; mesh generation; tree data structures; adaptive mesh refinement simulations; cosmological AMR algorithm; cosmological applications; data management; diffuse gas; galaxy cluster formation; global coarse grid; high resolution grids; local mesh refinement; multiscale phenomena; remote serving data; stars; visualization strategies; Adaptive mesh refinement; Animation; Clustering algorithms; Computational modeling; Data structures; Data visualization; Multidimensional systems; Navigation; Numerical simulation; Spatial resolution;
fLanguage :
English
Journal_Title :
Computing in Science & Engineering
Publisher :
ieee
ISSN :
1521-9615
Type :
jour
DOI :
10.1109/5992.774839
Filename :
774839
Link To Document :
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