Parallel peak pruning for scalable SMP contour tree computation
Parallel peak pruning for scalable SMP contour tree computation
复制标题
用于可扩展 SMP 轮廓树计算的并行峰值修剪
DOI:
10.1109/ldav.2016.7874312
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发表时间:
2016
期刊:
影响因子:
--
通讯作者:
Carr H
中科院分区:
文献类型:
--
作者:
Carr H
As data sets grow to exascale, automated data analysis and visualisation are increasingly important, to intermediate human understanding and to reduce demands on disk storage via in situ analysis. Trends in architecture of high performance computing systems necessitate analysis algorithms to make effective use of combinations of massively multicore and distributed systems. One of the principal analytic tools is the contour tree, which analyses relationships between contours to identify features of more than local importance. Unfortunately, the predominant algorithms for computing the contour tree are explicitly serial, and founded on serial metaphors, which has limited the scalability of this form of analysis. While there is some work on distributed contour tree computation, and separately on hybrid GPU-CPU computation, there is no efficient algorithm with strong formal guarantees on performance allied with fast practical performance. We report the first shared SMP algorithm for fully parallel contour tree computation, withfor-mal guarantees of O(lgnlgt) parallel steps and O(n lgn) work, and implementations with up to 10x parallel speed up in OpenMP and up to 50x speed up in NVIDIA Thrust.
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DOI:
--
发表时间:
2013
期刊:
ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming
影响因子:
--
作者:
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通讯作者:
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DOI:
--
发表时间:
2014
期刊:
Topological Methods in Data Analysis and Visualization
影响因子:
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作者:
D. Morozov;G. Weber
通讯作者:
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DOI:
10.1007/978-3-540-88606-8_5
发表时间:
2009
期刊:
SC14: International Conference for High Performance Computing, Networking, Storage and Analysis
影响因子:
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作者:
Hamish A. Carr;J. Snoeyink
通讯作者:
J. Snoeyink
影响因子:
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作者:
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通讯作者:
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DOI:
--
发表时间:
2005
期刊:
Computational geometry
影响因子:
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作者:
Yi;Tobias Lenz;Xiang Lu;G. Rote
通讯作者:
G. Rote