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
Y1 - 2013
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
U2 - 10.1145/2535571.2535591
DO - 10.1145/2535571.2535591
M3 - Conference contribution
AN - SCOPUS:84893010668
SN - 9781450325004
T3 - 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
BT - Proc. of UltraVis 2013
T2 - 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
Y2 - 17 November 2013 through 17 November 2013
ER -