Abstract
As the number of cores in processors increase and accelerator architectures are becoming more common, an ever greater number of threads is required to achieve full processor utilization. Our current parallel scientific visualization codes rely on partitioning data to achieve parallel processing, but this approach will not scale as we approach massive threading in which work is distributed in such a fine level that each thread is responsible for a minute portion of data. In this paper we characterize the challenges of refactoring our current visualization algorithms by considering the finest portion of work each performs and examining the domain of input data, overlaps of output domains, and interdepen-dencies among work instances. We divide our visualization algorithms into eight categories, each containing algorithms with the same interdependencies. By focusing our research efforts to solving these categorial challenges rather than this legion of individual algorithms, we can make attainable advancement for extreme computing.
| Original language | English (US) |
|---|---|
| Title of host publication | Proc. of UltraVis 2013 |
| Subtitle of host publication | 8th Int. Workshop on Ultrascale Visualization - Held in Conjunction with SC 2013: The Int. Conference for High Performance Computing, Networking, Storage and Analysis |
| DOIs | |
| State | Published - Dec 1 2013 |
| Event | 8th International Workshop on Ultrascale Visualization, UltraVis 2013 - Held in Conjunction with the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2013 - Denver, CO, United States Duration: Nov 17 2013 → Nov 17 2013 |
Other
| Other | 8th International Workshop on Ultrascale Visualization, UltraVis 2013 - Held in Conjunction with the International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2013 |
|---|---|
| Country | United States |
| City | Denver, CO |
| Period | 11/17/13 → 11/17/13 |
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ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications
Cite this
A classification of scientific visualization algorithms for massive threading. / Moreland, Kenneth; Geveci, Berk; Ma, Kwan-Liu; Maynard, Robert.
Proc. of UltraVis 2013: 8th Int. Workshop on Ultrascale Visualization - Held in Conjunction with SC 2013: The Int. Conference for High Performance Computing, Networking, Storage and Analysis. 2013. 2.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A classification of scientific visualization algorithms for massive threading
AU - Moreland, Kenneth
AU - Geveci, Berk
AU - Ma, Kwan-Liu
AU - Maynard, Robert
PY - 2013/12/1
Y1 - 2013/12/1
N2 - As the number of cores in processors increase and accelerator architectures are becoming more common, an ever greater number of threads is required to achieve full processor utilization. Our current parallel scientific visualization codes rely on partitioning data to achieve parallel processing, but this approach will not scale as we approach massive threading in which work is distributed in such a fine level that each thread is responsible for a minute portion of data. In this paper we characterize the challenges of refactoring our current visualization algorithms by considering the finest portion of work each performs and examining the domain of input data, overlaps of output domains, and interdepen-dencies among work instances. We divide our visualization algorithms into eight categories, each containing algorithms with the same interdependencies. By focusing our research efforts to solving these categorial challenges rather than this legion of individual algorithms, we can make attainable advancement for extreme computing.
AB - As the number of cores in processors increase and accelerator architectures are becoming more common, an ever greater number of threads is required to achieve full processor utilization. Our current parallel scientific visualization codes rely on partitioning data to achieve parallel processing, but this approach will not scale as we approach massive threading in which work is distributed in such a fine level that each thread is responsible for a minute portion of data. In this paper we characterize the challenges of refactoring our current visualization algorithms by considering the finest portion of work each performs and examining the domain of input data, overlaps of output domains, and interdepen-dencies among work instances. We divide our visualization algorithms into eight categories, each containing algorithms with the same interdependencies. By focusing our research efforts to solving these categorial challenges rather than this legion of individual algorithms, we can make attainable advancement for extreme computing.
UR - http://www.scopus.com/inward/record.url?scp=84893010668&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84893010668&partnerID=8YFLogxK
U2 - 10.1145/2535571.2535591
DO - 10.1145/2535571.2535591
M3 - Conference contribution
SN - 9781450325004
BT - Proc. of UltraVis 2013
ER -