| Parallel volume rendering and data coherence |
| Full text |
Pdf
(2.13 MB)
|
| Source
|
Parallel Rendering Symposium
archive
Proceedings of the 1993 symposium on Parallel rendering
table of contents
San Jose, California, United States
Pages: 23 - 26
Year of Publication: 1993
ISBN:0-89791-618-2
|
|
Authors
|
|
Brian Corrie
|
Australian National University, Canberra, ACT, Australia
|
|
Paul Mackerras
|
Australian National University, Canberra, ACT, Australia
|
|
| Sponsors |
|
| Publisher |
|
| Bibliometrics |
Downloads (6 Weeks): 6, Downloads (12 Months): 19, Citation Count: 11
|
|
|
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
| |
1
|
|
| |
2
|
Corrie, Brian and Paul Mackerras, "Parallel Volume Rendering and Data Coherence on the Fujitsu AP1000," Technical Report TR-CS-92-11, Department of Computer Science, Australian National University, August 1992.
|
| |
3
|
|
| |
4
|
Ishihata, Hiroaki, Takeshi Horie, and Toshiyuki Shimizu, "Architecture for the AP1000 Highly Parallel Computer," Fujitsu Scientific Fj Technical Journal 29, (1), pp. 6-14, March 1993.
|
| |
5
|
|
| |
6
|
|
 |
7
|
C. Montani , R. Perego , R. Scopigno, Parallel volume visualization on a hypercube architecture, Proceedings of the 1992 workshop on Volume visualization, p.9-16, October 19-20, 1992, Boston, Massachusetts, United States
[doi> 10.1145/147130.147139]
|
 |
8
|
|
 |
9
|
Guy Vézina , Peter A. Fletcher , Philip K. Robertson, Volume rendering on the MasPar MP-1, Proceedings of the 1992 workshop on Volume visualization, p.3-8, October 19-20, 1992, Boston, Massachusetts, United States
[doi> 10.1145/147130.147138]
|
| |
10
|
Terry S. Yoo , Ulrich Neumann , Henry Fuchs , Stephen M. Pizer , Tim Cullip , John Rhoades , Ross Whitaker, Direct Visualization of Volume Data, IEEE Computer Graphics and Applications, v.12 n.4, p.63-71, July 1992
[doi> 10.1109/38.144828]
|
CITED BY 11
|
|
M. Meißner , S. Grimm , W. Straßer , J. Packer , D. Latimer, Parallel volume rendering on a single-chip SIMD architecture, Proceedings of the IEEE 2001 symposium on parallel and large-data visualization and graphics, October 22-23, 2001, San Diego, California
|
|
|
P. Peggy Li , Scott Whitman , Roberto Mendoza , James Tsaio, ParVox: a parallel splatting volume rendering system for distributed visualization, Proceedings of the IEEE symposium on Parallel rendering, p.7-ff., October 20-21, 1997, Phoenix, Arizona, United States
|
|
|
Rändy Osborne , Hanspeter Pfister , Hugh Lauer , TakaHide Ohkami , Neil McKenzie , Sarah Gibson , Wally Hiatt, EM-Cube: an architecture for low-cost real-time volume rendering, Proceedings of the ACM SIGGRAPH/EUROGRAPHICS workshop on Graphics hardware, p.131-138, August 03-04, 1997, Los Angeles, California, United States
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
INDEX TERMS
Primary Classification:
I.
Computing Methodologies
I.3
COMPUTER GRAPHICS
I.3.1
Hardware architecture
Subjects:
Parallel processing
Additional Classification:
C.
Computer Systems Organization
C.1
PROCESSOR ARCHITECTURES
C.1.2
Multiple Data Stream Architectures (Multiprocessors)
Subjects:
Multiple-instruction-stream, multiple-data-stream processors (MIMD)
I.
Computing Methodologies
I.3
COMPUTER GRAPHICS
I.3.5
Computational Geometry and Object Modeling
Subjects:
Curve, surface, solid, and object representations
I.3.7
Three-Dimensional Graphics and Realism
Subjects:
Raytracing
General Terms:
Algorithms,
Design,
Measurement
Keywords:
distributed virtual memory,
image space,
visualization,
volume rendering,
worker farm
REVIEWS
"Jan Van den Bos : Reviewer"
Because of the sizes of the data sets (over 128 Mb) involved, a
data parallel approach is often preferred in parallel volume rendering.
With this approach the data are partitioned and distributed over the
nodes, and tasks are assigned to nodes
more...
"Frederik W. Jansen : Reviewer"
Because of the sizes of the data sets (over 128 MB) involved, a
data parallel approach is often preferred in parallel volume rendering.
With this approach, the data are partitioned and distributed over the
nodes, and tasks are assigned to node
more...
|