Adaptive, multiresolution visualization of large data sets using parallel octrees.

Conference · Thu Jun 10 04:00:00 EDT 1999

OSTI ID:11842

Freitag, L A; Loy, R M

The interactive visualization and exploration of large scientific data sets is a challenging and difficult task; their size often far exceeds the performance and memory capacity of even the most powerful graphics work-stations. To address this problem, we have created a technique that combines hierarchical data reduction methods with parallel computing to allow interactive exploration of large data sets while retaining full-resolution capability. The hierarchical representation is built in parallel by strategically inserting field data into an octree data structure. We provide functionality that allows the user to interactively adapt the resolution of the reduced data sets so that resolution is increased in regions of interest without sacrificing local graphics performance. We describe the creation of the reduced data sets using a parallel octree, the software architecture of the system, and the performance of this system on the data from a Rayleigh-Taylor instability simulation.

• Research Organization: Argonne National Lab., IL (US)
• Sponsoring Organization: US Department of Energy (US)
• DOE Contract Number: W-31109-ENG-38
• OSTI ID: 11842
• Report Number(s): ANL/MCS/CP-99211
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS
CAPACITY
COMPUTER ARCHITECTURE
COMPUTER GRAPHICS
COMPUTERIZED SIMULATION
DATA PROCESSING
PARALLEL PROCESSING
PERFORMANCE
RAYLEIGH-TAYLOR INSTABILITY
RESOLUTION