Abstract
Changes are needed in the way that visualization is performed, if we expect the analysis of scientific data to be effective at the petascale and beyond. By using similar techniques as those used to parallelize simulations, such as parallel I/O, load balancing, and effective use of interprocess communication, the supercomputers that compute these datasets can also serve as analysis and visualization engines for them. Our team is assessing the feasibility of performing parallel scientific visualization on some of the most powerful computational resources of the U.S. Department of Energy's National Laboratories in order to pave the way for analyzing the next generation of computational results. This paper highlights some of the conclusions of that research.
| Original language | English (US) |
|---|---|
| Article number | 012088 |
| Journal | Journal of Physics: Conference Series |
| Volume | 180 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 1 2009 |
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ASJC Scopus subject areas
- Physics and Astronomy(all)
Cite this
Parallel visualization on leadership computing resources. / Peterka, T.; Ross, R. B.; Shen, H. W.; Ma, Kwan-Liu; Kendall, W.; Yu, H.
In: Journal of Physics: Conference Series, Vol. 180, No. 1, 012088, 01.01.2009.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - Parallel visualization on leadership computing resources
AU - Peterka, T.
AU - Ross, R. B.
AU - Shen, H. W.
AU - Ma, Kwan-Liu
AU - Kendall, W.
AU - Yu, H.
PY - 2009/1/1
Y1 - 2009/1/1
N2 - Changes are needed in the way that visualization is performed, if we expect the analysis of scientific data to be effective at the petascale and beyond. By using similar techniques as those used to parallelize simulations, such as parallel I/O, load balancing, and effective use of interprocess communication, the supercomputers that compute these datasets can also serve as analysis and visualization engines for them. Our team is assessing the feasibility of performing parallel scientific visualization on some of the most powerful computational resources of the U.S. Department of Energy's National Laboratories in order to pave the way for analyzing the next generation of computational results. This paper highlights some of the conclusions of that research.
AB - Changes are needed in the way that visualization is performed, if we expect the analysis of scientific data to be effective at the petascale and beyond. By using similar techniques as those used to parallelize simulations, such as parallel I/O, load balancing, and effective use of interprocess communication, the supercomputers that compute these datasets can also serve as analysis and visualization engines for them. Our team is assessing the feasibility of performing parallel scientific visualization on some of the most powerful computational resources of the U.S. Department of Energy's National Laboratories in order to pave the way for analyzing the next generation of computational results. This paper highlights some of the conclusions of that research.
UR - http://www.scopus.com/inward/record.url?scp=77957094479&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77957094479&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/180/1/012088
DO - 10.1088/1742-6596/180/1/012088
M3 - Conference article
VL - 180
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
SN - 1742-6588
IS - 1
M1 - 012088
ER -