TY - GEN
T1 - A unified infrastructure for parallel out-of-core isosurface extraction and volume rendering of unstructured grids
AU - Chiang, Yi Jen
AU - Farias, R.
AU - Silva, C. T.
AU - Wei, Bin
N1 - Publisher Copyright:
© 2001 IEEE.
PY - 2001
Y1 - 2001
N2 - We present a unified infrastructure for parallel out-of-core isosurface extraction and volume rendering of large unstructured grids on distributed-memory parallel machines. We parallelize the out-of-core isosurface extraction algorithm of Chiang et al. (1998) and the out-of-core ZSweep technique (Farias and Silva, 2001) for direct volume rendering, using the meta-cell technique as a unified underlying building block. Our one-time preprocessing first partitions the dataset into meta-cells that are stored in disk. From the meta-cells, we build a BBIO tree in disk, which can be used to speed up isosurface extraction, and a bounding-box file in disk, which is used for direct volume rendering. At run-time, we use a simple self-scheduling scheme to achieve load balancing among the processors. We perform several experiments on a sixteen-node cluster of PCs connected by a gigabit Ethernet, using datasets as large as 6.6 million cells. For the larger datasets, we have found that both our isosurface extraction and direct volume rendering approaches are perfectly scalable up to sixteen nodes.
AB - We present a unified infrastructure for parallel out-of-core isosurface extraction and volume rendering of large unstructured grids on distributed-memory parallel machines. We parallelize the out-of-core isosurface extraction algorithm of Chiang et al. (1998) and the out-of-core ZSweep technique (Farias and Silva, 2001) for direct volume rendering, using the meta-cell technique as a unified underlying building block. Our one-time preprocessing first partitions the dataset into meta-cells that are stored in disk. From the meta-cells, we build a BBIO tree in disk, which can be used to speed up isosurface extraction, and a bounding-box file in disk, which is used for direct volume rendering. At run-time, we use a simple self-scheduling scheme to achieve load balancing among the processors. We perform several experiments on a sixteen-node cluster of PCs connected by a gigabit Ethernet, using datasets as large as 6.6 million cells. For the larger datasets, we have found that both our isosurface extraction and direct volume rendering approaches are perfectly scalable up to sixteen nodes.
KW - Isosurface Extraction
KW - Out-Of-Core Techniques
KW - Parallel Computation
KW - Scientific Visualization
KW - Volume Rendering
UR - http://www.scopus.com/inward/record.url?scp=84963567967&partnerID=8YFLogxK
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U2 - 10.1109/PVGS.2001.964405
DO - 10.1109/PVGS.2001.964405
M3 - Conference contribution
AN - SCOPUS:84963567967
T3 - Proceedings - IEEE 2001 Symposium on Parallel and Large-Data Visualization and Graphics
SP - 59
EP - 66
BT - Proceedings - IEEE 2001 Symposium on Parallel and Large-Data Visualization and Graphics
A2 - Spencer, Stephen N.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Symposium on Parallel and Large-Data Visualization and Graphics
Y2 - 22 October 2001 through 23 October 2001
ER -