Scalable Contour Tree Computation by Data Parallel Peak Pruning

Journal Article · Fri Nov 01 00:00:00 EDT 2019 · IEEE Transactions on Visualization and Computer Graphics

DOI:https://doi.org/10.1109/tvcg.2019.2948616· OSTI ID:1797740

Carr, Hamish A. ^[1]; Weber, Gunther H. ^[2]; Sewell, Christopher M. ^[3]; Rubel, Oliver ^[4]; Fasel, Patricia ^[3]; Ahrens, James P. ^[3]

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1. Univ. of Leeds (United Kingdom)
2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Davis, CA (United States)
3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
4. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

As data sets grow to exascale, automated data analysis and visualization 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. Furthermore, we report the first shared SMP algorithm for fully parallel contour tree computation, with formal guarantees of O(lg V lg t) parallel steps and O(V lg V) work for data with V samples and t contour tree supernodes, and implementations with more than 30× parallel speed up on both CPU using TBB and GPU using Thrust and up 70× speed up compared to the serial sweep and merge algorithm.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); USDOE Office of Science (SC), Basic Energy Sciences (BES). Scientific User Facilities Division
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1797740
• Journal Information: IEEE Transactions on Visualization and Computer Graphics, Journal Name: IEEE Transactions on Visualization and Computer Graphics Journal Issue: 4 Vol. 27; ISSN 1077-2626
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
Topological analysis
computational geometry and object modeling
computer graphics
contour tree
curve
data parallel algorithms
merge tree
simulation and modeling
simulation output analysis
solid and object representations
surface