Out-of-core streamline visualization on large unstructured meshes

This paper presents an out-of-core approach for interactive streamline construction on large unstructured tetrahedral meshes containing millions of elements. The out-of-core algorithm uses an octree to partition and restructure the raw data into subsets stored into disk files for fast data retrieval...

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Vydané v:IEEE transactions on visualization and computer graphics Ročník 3; číslo 4; s. 370 - 380
Hlavní autori: Shyh-Kuang Ueng, Sikorski, C., Kwan-Liu Ma
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY IEEE 01.10.1997
IEEE Computer Society
Predmet:
Algorithmics. Computability. Computer arithmetics
Application software
Applied sciences
Artificial intelligence
Computational fluid dynamics
Computer science; control theory; systems
Concurrent computing
Data visualization
Distributed computing
Exact sciences and technology
Information retrieval
Memory management
Partitioning algorithms
Pattern recognition. Digital image processing. Computational geometry
Processor scheduling
Theoretical computing
Workstations
System architecture
Visualization
Grid pattern
Computational fluid dynamics
Image processing
Experimental study
Algorithm
Interactive system
Computational geometry
System performance
Storage management
Hard disk
Data structure
ISSN:1077-2626
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Shrnutí:This paper presents an out-of-core approach for interactive streamline construction on large unstructured tetrahedral meshes containing millions of elements. The out-of-core algorithm uses an octree to partition and restructure the raw data into subsets stored into disk files for fast data retrieval. A memory management policy tailored to the streamline calculations is used such that, during the streamline construction, only a very small amount of data are brought into the main memory on demand. By carefully scheduling computation and data fetching, the overhead of reading data from the disk is significantly reduced and good memory performance results. This out-of-core algorithm makes possible interactive streamline visualization of large unstructured-grid data sets on a single mid-range workstation with relatively low main-memory capacity: 5-15 megabytes. We also demonstrate that this approach is much more efficient than relying on virtual memory and operating system's paging algorithms.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
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ISSN:1077-2626
DOI:10.1109/2945.646239