Abstract
Adaptive Mesh Refinement is a popular approach for allocating scarce computing resources to the most important portions of the simulation domain. This approach implies spatial compression and the large simulation sizes which necessitate it. We present a novel, cluster- and GPU-parallel rendering scheme for AMR data, which is built on previous work in the GPU ray casting of AMR data. Our approach utilizes the existing AMR structure to subdivide the problem into convexly-bounded chunks and perform static load-balancing. We take advantage of data locality within chunks to interpolate directly between blocks without the need to store ghost cells on the interior boundaries. We also present a novel block decomposition method, and analyze its performance against two alternative methods. Finally, we examine the interactivity of our renderer for multiple datasets, and consider its scalability across a large number of GPUs.
| Original language | English (US) |
|---|---|
| Pages | 35-42 |
| Number of pages | 8 |
| DOIs | |
| State | Published - Jan 1 2013 |
| Event | 2013 3rd IEEE Symposium on Large-Scale Data Analysis and Visualization, LDAV 2013 - Atlanta, GA, United States Duration: Oct 13 2013 → Oct 14 2013 |
Other
| Other | 2013 3rd IEEE Symposium on Large-Scale Data Analysis and Visualization, LDAV 2013 |
|---|---|
| Country | United States |
| City | Atlanta, GA |
| Period | 10/13/13 → 10/14/13 |
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Keywords
- adaptive mesh refinement
- Volume rendering
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
Cite this
Efficient parallel volume rendering of large-scale adaptive mesh refinement data. / Leaf, Nick; Vishwanath, Venkatram; Insley, Joseph; Hereld, Mark; Papka, Michael E.; Ma, Kwan-Liu.
2013. 35-42 Paper presented at 2013 3rd IEEE Symposium on Large-Scale Data Analysis and Visualization, LDAV 2013, Atlanta, GA, United States.Research output: Contribution to conference › Paper
}
TY - CONF
T1 - Efficient parallel volume rendering of large-scale adaptive mesh refinement data
AU - Leaf, Nick
AU - Vishwanath, Venkatram
AU - Insley, Joseph
AU - Hereld, Mark
AU - Papka, Michael E.
AU - Ma, Kwan-Liu
PY - 2013/1/1
Y1 - 2013/1/1
N2 - Adaptive Mesh Refinement is a popular approach for allocating scarce computing resources to the most important portions of the simulation domain. This approach implies spatial compression and the large simulation sizes which necessitate it. We present a novel, cluster- and GPU-parallel rendering scheme for AMR data, which is built on previous work in the GPU ray casting of AMR data. Our approach utilizes the existing AMR structure to subdivide the problem into convexly-bounded chunks and perform static load-balancing. We take advantage of data locality within chunks to interpolate directly between blocks without the need to store ghost cells on the interior boundaries. We also present a novel block decomposition method, and analyze its performance against two alternative methods. Finally, we examine the interactivity of our renderer for multiple datasets, and consider its scalability across a large number of GPUs.
AB - Adaptive Mesh Refinement is a popular approach for allocating scarce computing resources to the most important portions of the simulation domain. This approach implies spatial compression and the large simulation sizes which necessitate it. We present a novel, cluster- and GPU-parallel rendering scheme for AMR data, which is built on previous work in the GPU ray casting of AMR data. Our approach utilizes the existing AMR structure to subdivide the problem into convexly-bounded chunks and perform static load-balancing. We take advantage of data locality within chunks to interpolate directly between blocks without the need to store ghost cells on the interior boundaries. We also present a novel block decomposition method, and analyze its performance against two alternative methods. Finally, we examine the interactivity of our renderer for multiple datasets, and consider its scalability across a large number of GPUs.
KW - adaptive mesh refinement
KW - Volume rendering
UR - http://www.scopus.com/inward/record.url?scp=84899089535&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84899089535&partnerID=8YFLogxK
U2 - 10.1109/LDAV.2013.6675156
DO - 10.1109/LDAV.2013.6675156
M3 - Paper
SP - 35
EP - 42
ER -