Abstract
This talk presents a new approach to distance visualization of very large data sets output from scientific supercomputing. The processing power of massively parallel supercomputers increases at a rather fast rate, about an order of magnitude faster every three years, enabling scientists to model complex physical phenomena and chemical processes at unprecedented fidelity. Several petascale computers are already in operation (http://www.top500.org) and exascale computing is around the corner. Each run of a petascale simulation typically outputs several hundred terabytes of data to disk. Transferring data at this scale over wide-area networks to the scientist's laboratory for post-processing analysis is not an option. Even the data files may be transferred, existing desktop data analysis and visualization tools cannot effectively handle such large-scale data. If the scientists may use the same supercomputing facility for data analysis and visualization, there are three viable solutions: in situ visualization, where visualization is computed during the simulation on the same supercomputer co-processing visualization, where visualization is computed during the simulation on a separate computer, and post-processing visualization, where visualization is computed after simulation is over.
| Original language | English (US) |
|---|---|
| Title of host publication | ACM SIGGRAPH 2010 Talks, SIGGRAPH '10 |
| DOIs | |
| State | Published - Sep 10 2010 |
| Event | ACM SIGGRAPH 2010 Talks, SIGGRAPH '10 - Los Angeles, CA, United States Duration: Jul 26 2010 → Jul 30 2010 |
Other
| Other | ACM SIGGRAPH 2010 Talks, SIGGRAPH '10 |
|---|---|
| Country | United States |
| City | Los Angeles, CA |
| Period | 7/26/10 → 7/30/10 |
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ASJC Scopus subject areas
- Computer Graphics and Computer-Aided Design
- Computer Vision and Pattern Recognition
- Software
Cite this
Distance visualization of ultrascale data with explorable images. / Ma, Kwan-Liu; Tikhonova, Anna; Correa, Carlos D.
ACM SIGGRAPH 2010 Talks, SIGGRAPH '10. 2010. 9.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Distance visualization of ultrascale data with explorable images
AU - Ma, Kwan-Liu
AU - Tikhonova, Anna
AU - Correa, Carlos D.
PY - 2010/9/10
Y1 - 2010/9/10
N2 - This talk presents a new approach to distance visualization of very large data sets output from scientific supercomputing. The processing power of massively parallel supercomputers increases at a rather fast rate, about an order of magnitude faster every three years, enabling scientists to model complex physical phenomena and chemical processes at unprecedented fidelity. Several petascale computers are already in operation (http://www.top500.org) and exascale computing is around the corner. Each run of a petascale simulation typically outputs several hundred terabytes of data to disk. Transferring data at this scale over wide-area networks to the scientist's laboratory for post-processing analysis is not an option. Even the data files may be transferred, existing desktop data analysis and visualization tools cannot effectively handle such large-scale data. If the scientists may use the same supercomputing facility for data analysis and visualization, there are three viable solutions: in situ visualization, where visualization is computed during the simulation on the same supercomputer co-processing visualization, where visualization is computed during the simulation on a separate computer, and post-processing visualization, where visualization is computed after simulation is over.
AB - This talk presents a new approach to distance visualization of very large data sets output from scientific supercomputing. The processing power of massively parallel supercomputers increases at a rather fast rate, about an order of magnitude faster every three years, enabling scientists to model complex physical phenomena and chemical processes at unprecedented fidelity. Several petascale computers are already in operation (http://www.top500.org) and exascale computing is around the corner. Each run of a petascale simulation typically outputs several hundred terabytes of data to disk. Transferring data at this scale over wide-area networks to the scientist's laboratory for post-processing analysis is not an option. Even the data files may be transferred, existing desktop data analysis and visualization tools cannot effectively handle such large-scale data. If the scientists may use the same supercomputing facility for data analysis and visualization, there are three viable solutions: in situ visualization, where visualization is computed during the simulation on the same supercomputer co-processing visualization, where visualization is computed during the simulation on a separate computer, and post-processing visualization, where visualization is computed after simulation is over.
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U2 - 10.1145/1837026.1837038
DO - 10.1145/1837026.1837038
M3 - Conference contribution
AN - SCOPUS:77956310802
SN - 9781450303941
BT - ACM SIGGRAPH 2010 Talks, SIGGRAPH '10
ER -