Abstract
As computing technology continues to advance, computational modeling of scientific and engineering problems produces data of increasing complexity: large in size and unstructural in shape. Volume visualization of such data is a challenging problem. This paper proposes a distributed parallel solution that makes ray-casting volume rendering of unstructured-grid data practical. Both the data and the rendering process are distributed among processors. At each processor, ray-casting of local data is performed independent of the other processors. The global image compositing processes, which require inter-processor communication, are overlapped with the local ray-casting processes to achieve maximum parallel efficiency. This algorithm differs from previous ones in four ways: it is completely distributed, less view-dependent, reasonably scalable, and flexible. Without dynamic load balancing, test results on the Intel Paragon using from two to 128 processors show, on average, about 60% parallel efficiency.
| Original language | English (US) |
|---|---|
| Pages | 23-30 |
| Number of pages | 8 |
| State | Published - Dec 1 1995 |
| Externally published | Yes |
| Event | Proceedings of the 1995 Parallel Rendering Symposium - Atlanta, GA, USA Duration: Oct 30 1995 → Oct 31 1995 |
Other
| Other | Proceedings of the 1995 Parallel Rendering Symposium |
|---|---|
| City | Atlanta, GA, USA |
| Period | 10/30/95 → 10/31/95 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
Parallel volume ray-casting for unstructured-grid data on distributed-memory architectures. / Ma, Kwan-Liu.
1995. 23-30 Paper presented at Proceedings of the 1995 Parallel Rendering Symposium, Atlanta, GA, USA, .Research output: Contribution to conference › Paper
}
TY - CONF
T1 - Parallel volume ray-casting for unstructured-grid data on distributed-memory architectures
AU - Ma, Kwan-Liu
PY - 1995/12/1
Y1 - 1995/12/1
N2 - As computing technology continues to advance, computational modeling of scientific and engineering problems produces data of increasing complexity: large in size and unstructural in shape. Volume visualization of such data is a challenging problem. This paper proposes a distributed parallel solution that makes ray-casting volume rendering of unstructured-grid data practical. Both the data and the rendering process are distributed among processors. At each processor, ray-casting of local data is performed independent of the other processors. The global image compositing processes, which require inter-processor communication, are overlapped with the local ray-casting processes to achieve maximum parallel efficiency. This algorithm differs from previous ones in four ways: it is completely distributed, less view-dependent, reasonably scalable, and flexible. Without dynamic load balancing, test results on the Intel Paragon using from two to 128 processors show, on average, about 60% parallel efficiency.
AB - As computing technology continues to advance, computational modeling of scientific and engineering problems produces data of increasing complexity: large in size and unstructural in shape. Volume visualization of such data is a challenging problem. This paper proposes a distributed parallel solution that makes ray-casting volume rendering of unstructured-grid data practical. Both the data and the rendering process are distributed among processors. At each processor, ray-casting of local data is performed independent of the other processors. The global image compositing processes, which require inter-processor communication, are overlapped with the local ray-casting processes to achieve maximum parallel efficiency. This algorithm differs from previous ones in four ways: it is completely distributed, less view-dependent, reasonably scalable, and flexible. Without dynamic load balancing, test results on the Intel Paragon using from two to 128 processors show, on average, about 60% parallel efficiency.
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M3 - Paper
AN - SCOPUS:0029544349
SP - 23
EP - 30
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