Abstract
The development of supercomputers has greatly help us to carry on large-scale computing for dealing with various problems through simulating and analyzing them. Visualization is an indispensable tool to understand the properties of the data from supercomputers. Especially, interactive visualization can help us to analyze data from various viewpoints and even to find out some local small but important features. However, it is still difficult to interactively visualize such kind of big data directly due to the slow file I/O problem and the limitation of memory size. For resolving these problems, we proposed a parallel compression method to reduce the data size with low computational cost. Furthermore, the fast linear decompression process is another merit for interactive visualization. Our method uses proper orthogonal decomposition (POD) to compress data because it can effectively extract important features from the data and the resulting compressed data can also be linearly decompressed. Our implementation achieves high parallel efficiency with a binary load-distributed approach, which is similar to the binary-swap image composition used in parallel volume rendering [2]. This approach allows us to effectively utilize all the processing nodes and reduce the interprocessor communication cost throughout the parallel compression calculations. Our test results on the K computer demonstrate superior performance of our design and implementation.
| Original language | English |
|---|---|
| Title of host publication | IEEE Symposium on Large Data Analysis and Visualization 2013, LDAV 2013 - Proceedings |
| Publisher | IEEE Computer Society |
| Pages | 121-122 |
| Number of pages | 2 |
| DOIs | |
| Publication status | Published - Jan 1 2013 |
| Externally published | Yes |
| 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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All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
Cite this
Proper orthogonal decomposition based parallel compression for visualizing big data on the K computer. / Bi, Chongke; Ono, Kenji; Ma, Kwan Liu; Wu, Haiyuan; Imamura, Toshiyuki.
IEEE Symposium on Large Data Analysis and Visualization 2013, LDAV 2013 - Proceedings. IEEE Computer Society, 2013. p. 121-122 6675169.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Proper orthogonal decomposition based parallel compression for visualizing big data on the K computer
AU - Bi, Chongke
AU - Ono, Kenji
AU - Ma, Kwan Liu
AU - Wu, Haiyuan
AU - Imamura, Toshiyuki
PY - 2013/1/1
Y1 - 2013/1/1
N2 - The development of supercomputers has greatly help us to carry on large-scale computing for dealing with various problems through simulating and analyzing them. Visualization is an indispensable tool to understand the properties of the data from supercomputers. Especially, interactive visualization can help us to analyze data from various viewpoints and even to find out some local small but important features. However, it is still difficult to interactively visualize such kind of big data directly due to the slow file I/O problem and the limitation of memory size. For resolving these problems, we proposed a parallel compression method to reduce the data size with low computational cost. Furthermore, the fast linear decompression process is another merit for interactive visualization. Our method uses proper orthogonal decomposition (POD) to compress data because it can effectively extract important features from the data and the resulting compressed data can also be linearly decompressed. Our implementation achieves high parallel efficiency with a binary load-distributed approach, which is similar to the binary-swap image composition used in parallel volume rendering [2]. This approach allows us to effectively utilize all the processing nodes and reduce the interprocessor communication cost throughout the parallel compression calculations. Our test results on the K computer demonstrate superior performance of our design and implementation.
AB - The development of supercomputers has greatly help us to carry on large-scale computing for dealing with various problems through simulating and analyzing them. Visualization is an indispensable tool to understand the properties of the data from supercomputers. Especially, interactive visualization can help us to analyze data from various viewpoints and even to find out some local small but important features. However, it is still difficult to interactively visualize such kind of big data directly due to the slow file I/O problem and the limitation of memory size. For resolving these problems, we proposed a parallel compression method to reduce the data size with low computational cost. Furthermore, the fast linear decompression process is another merit for interactive visualization. Our method uses proper orthogonal decomposition (POD) to compress data because it can effectively extract important features from the data and the resulting compressed data can also be linearly decompressed. Our implementation achieves high parallel efficiency with a binary load-distributed approach, which is similar to the binary-swap image composition used in parallel volume rendering [2]. This approach allows us to effectively utilize all the processing nodes and reduce the interprocessor communication cost throughout the parallel compression calculations. Our test results on the K computer demonstrate superior performance of our design and implementation.
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UR - http://www.scopus.com/inward/citedby.url?scp=84899128400&partnerID=8YFLogxK
U2 - 10.1109/LDAV.2013.6675169
DO - 10.1109/LDAV.2013.6675169
M3 - Conference contribution
AN - SCOPUS:84899128400
SP - 121
EP - 122
BT - IEEE Symposium on Large Data Analysis and Visualization 2013, LDAV 2013 - Proceedings
PB - IEEE Computer Society
ER -