Parallel peak pruning for scalable SMP contour tree computation

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 ef...

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Vydáno v:2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV) s. 75 - 84
Hlavní autoři: Carr, Hamish A., Weber, Gunther H., Sewell, Christopher M., Ahrens, James P.
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2016
Témata:
Algorithm design and analysis
Analytical models
Computational modeling
contour tree
Data models
data parallel algorithms
merge tree
Solid modeling
topological analysis
Topology
Vegetation
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Shrnutí: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, with formal guarantees of O(lg n lg t) parallel steps and O(n lg n) work, and implementations with up to 10× parallel speed up in OpenMP and up to 50× speed up in NVIDIA Thrust.
DOI:10.1109/LDAV.2016.7874312