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Title: Visualization Tools for Adaptive Mesh Refinement Data

Abstract

Adaptive Mesh Refinement (AMR) is a highly effective method for simulations that span a large range of spatiotemporal scales, such as astrophysical simulations that must accommodate ranges from interstellar to sub-planetary. Most mainstream visualization tools still lack support for AMR as a first class data type and AMR code teams use custom built applications for AMR visualization. The Department of Energy's (DOE's) Science Discovery through Advanced Computing (SciDAC) Visualization and Analytics Center for Enabling Technologies (VACET) is currently working on extending VisIt, which is an open source visualization tool that accommodates AMR as a first-class data type. These efforts will bridge the gap between general-purpose visualization applications and highly specialized AMR visual analysis applications. Here, we give an overview of the state of the art in AMR visualization research and tools and describe how VisIt currently handles AMR data.

Authors:
; ; ; ; ; ;
Publication Date:
Research Org.:
Ernest Orlando Lawrence Berkeley NationalLaboratory, Berkeley, CA (US)
Sponsoring Org.:
USDOE Director. Office of Science. Advanced ScientificComputing Research
OSTI Identifier:
925532
Report Number(s):
LBNL-62954
R&D Project: KS0612; BnR: KJ0101030; TRN: US200809%%681
DOE Contract Number:
DE-AC02-05CH11231
Resource Type:
Conference
Resource Relation:
Conference: 4th High-End Visualization Workshop, Obergrugl,Austria, 06/17-06/22
Country of Publication:
United States
Language:
English
Subject:
42; A CODES; MESH GENERATION; COMPUTER GRAPHICS; COMPUTER CALCULATIONS

Citation Formats

Weber, Gunther H., Beckner, Vincent E., Childs, Hank, Ligocki,Terry J., Miller, Mark C., Van Straalen, Brian, and Bethel, E. Wes. Visualization Tools for Adaptive Mesh Refinement Data. United States: N. p., 2007. Web.
Weber, Gunther H., Beckner, Vincent E., Childs, Hank, Ligocki,Terry J., Miller, Mark C., Van Straalen, Brian, & Bethel, E. Wes. Visualization Tools for Adaptive Mesh Refinement Data. United States.
Weber, Gunther H., Beckner, Vincent E., Childs, Hank, Ligocki,Terry J., Miller, Mark C., Van Straalen, Brian, and Bethel, E. Wes. Wed . "Visualization Tools for Adaptive Mesh Refinement Data". United States. doi:. https://www.osti.gov/servlets/purl/925532.
@article{osti_925532,
title = {Visualization Tools for Adaptive Mesh Refinement Data},
author = {Weber, Gunther H. and Beckner, Vincent E. and Childs, Hank and Ligocki,Terry J. and Miller, Mark C. and Van Straalen, Brian and Bethel, E. Wes},
abstractNote = {Adaptive Mesh Refinement (AMR) is a highly effective method for simulations that span a large range of spatiotemporal scales, such as astrophysical simulations that must accommodate ranges from interstellar to sub-planetary. Most mainstream visualization tools still lack support for AMR as a first class data type and AMR code teams use custom built applications for AMR visualization. The Department of Energy's (DOE's) Science Discovery through Advanced Computing (SciDAC) Visualization and Analytics Center for Enabling Technologies (VACET) is currently working on extending VisIt, which is an open source visualization tool that accommodates AMR as a first-class data type. These efforts will bridge the gap between general-purpose visualization applications and highly specialized AMR visual analysis applications. Here, we give an overview of the state of the art in AMR visualization research and tools and describe how VisIt currently handles AMR data.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed May 09 00:00:00 EDT 2007},
month = {Wed May 09 00:00:00 EDT 2007}
}

Conference:
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  • Adaptive Mesh Refinement (AMR) is a highly effective computation method for simulations that span a large range of spatiotemporal scales, such as astrophysical simulations, which must accommodate ranges from interstellar to sub-planetary. Most mainstream visualization tools still lack support for AMR grids as a first class data type and AMR code teams use custom built applications for AMR visualization. The Department of Energy's (DOE's) Science Discovery through Advanced Computing (SciDAC) Visualization and Analytics Center for Enabling Technologies (VACET) is currently working on extending VisIt, which is an open source visualization tool that accommodates AMR as a first-class data type. Thesemore » efforts will bridge the gap between general-purpose visualization applications and highly specialized AMR visual analysis applications. Here, we give an overview of the state of the art in AMR scalar data visualization research.« less
  • The adaptive mesh refinement method for solving partial differential equations subdivides the grid in regions of high variation, where finer grids are necessary to produce accurate solutions, while keeping coarser grids where they are adequate. Visualization techniques which take advantage of volume coherence can efficiently render the larger grid cells, and concentrate computation time on the finely subdivided regions containing the details of interest.
  • The adaptive mesh refinement method for solving partial differential equations subdivides the grid in regions of high variation, where finer grids are necessary to produce accurate solutions, while keeping coarser grids where they are adequate. Visualization techniques which take advantage of volume coherence can efficiently render the larger grid cells, and concentrate computation time on the finely subdivided regions containing the details of interest.
  • The visualization and analysis of AMR-based simulations is integral to the process of obtaining new insight in scientific research. We present a new method for performing query-driven visualization and analysis on AMR data, with specific emphasis on time-varying AMR data. Our work introduces a new method that directly addresses the dynamic spatial and temporal properties of AMR grids which challenge many existing visualization techniques. Further, we present the first implementation of query-driven visualization on the GPU that uses a GPU-based indexing structure to both answer queries and efficiently utilize GPU memory. We apply our method to two different science domainsmore » to demonstrate its broad applicability.« less