Hybrid Parallel Extraction of Isosurface Components from 3D Rectilinear Volume Data
Summary :
We describe an efficient algorithm that extracts a connected component of an isosurface, or a contour, from a 3D rectilinear volume data. The efficiency of the algorithm is achieved by three factors: (i) directly working with rectilinear grids, (ii) parallel utilization of a multi-core CPU for extracting active cells, the cells containing the contour, and (iii) parallel utilization of a many-core GPU for computing the geometries of a contour surface in each active cell using CUDA. Experimental results show that our hybrid parallel implementation achieved up to 20x speedup over existing methods on an ordinary PC. Our work coupled with the Contour Tree framework is useful for quickly segmenting, displaying, and analyzing a feature of interest in 3D rectilinear volume data without being distracted by other features.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.12 pp.2553-2556
- Publication Date
- 2011/12/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.2553
- Type of Manuscript
- LETTER
- Category
- Computer Graphics
Authors
Keyword
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Bong-Soo SOHN, "Hybrid Parallel Extraction of Isosurface Components from 3D Rectilinear Volume Data" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 12, pp. 2553-2556, December 2011, doi: 10.1587/transinf.E94.D.2553.
Abstract: We describe an efficient algorithm that extracts a connected component of an isosurface, or a contour, from a 3D rectilinear volume data. The efficiency of the algorithm is achieved by three factors: (i) directly working with rectilinear grids, (ii) parallel utilization of a multi-core CPU for extracting active cells, the cells containing the contour, and (iii) parallel utilization of a many-core GPU for computing the geometries of a contour surface in each active cell using CUDA. Experimental results show that our hybrid parallel implementation achieved up to 20x speedup over existing methods on an ordinary PC. Our work coupled with the Contour Tree framework is useful for quickly segmenting, displaying, and analyzing a feature of interest in 3D rectilinear volume data without being distracted by other features.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.2553/_p
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@ARTICLE{e94-d_12_2553,
author={Bong-Soo SOHN, },
journal={IEICE TRANSACTIONS on Information},
title={Hybrid Parallel Extraction of Isosurface Components from 3D Rectilinear Volume Data},
year={2011},
volume={E94-D},
number={12},
pages={2553-2556},
abstract={We describe an efficient algorithm that extracts a connected component of an isosurface, or a contour, from a 3D rectilinear volume data. The efficiency of the algorithm is achieved by three factors: (i) directly working with rectilinear grids, (ii) parallel utilization of a multi-core CPU for extracting active cells, the cells containing the contour, and (iii) parallel utilization of a many-core GPU for computing the geometries of a contour surface in each active cell using CUDA. Experimental results show that our hybrid parallel implementation achieved up to 20x speedup over existing methods on an ordinary PC. Our work coupled with the Contour Tree framework is useful for quickly segmenting, displaying, and analyzing a feature of interest in 3D rectilinear volume data without being distracted by other features.},
keywords={},
doi={10.1587/transinf.E94.D.2553},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Hybrid Parallel Extraction of Isosurface Components from 3D Rectilinear Volume Data
T2 - IEICE TRANSACTIONS on Information
SP - 2553
EP - 2556
AU - Bong-Soo SOHN
PY - 2011
DO - 10.1587/transinf.E94.D.2553
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2011
AB - We describe an efficient algorithm that extracts a connected component of an isosurface, or a contour, from a 3D rectilinear volume data. The efficiency of the algorithm is achieved by three factors: (i) directly working with rectilinear grids, (ii) parallel utilization of a multi-core CPU for extracting active cells, the cells containing the contour, and (iii) parallel utilization of a many-core GPU for computing the geometries of a contour surface in each active cell using CUDA. Experimental results show that our hybrid parallel implementation achieved up to 20x speedup over existing methods on an ordinary PC. Our work coupled with the Contour Tree framework is useful for quickly segmenting, displaying, and analyzing a feature of interest in 3D rectilinear volume data without being distracted by other features.
ER -
English
