Large-scale in-situ topological analysis using segmented merge trees: performance, scalability, and power efficiency

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Title Large-scale in-situ topological analysis using segmented merge trees: performance, scalability, and power efficiency
Publication Type thesis
School or College College of Engineering
Department Computing
Author Landge, Aaditya G
Date 2016
Description The ever-increasing amounts of data generated by scientific simulations, coupled with system I/O constraints, are fueling a need for in-situ analysis techniques, i.e., performing the analysis concurrently with the simulation. Of particular interest are approaches that produce reduced data representations while maintaining the ability to redefine, extract, and study features in a postprocess to obtain scientific insights. One such approach is using topological constructs called segmented merge trees, which record changes in the topology of super-level sets of a scalar function. They encapsulate a wide range of threshold-based features, which can be extracted for analysis and visualization; however, current techniques for their computation are not scalable enough for in-situ analysis. This thesis presents a novel distributed algorithm that, for the first time, allows large-scale, in-situ computation of segmented merge trees. Existing merge tree computation techniques are restricted to simplicial complexes and three-dimensional (3D) rectilinear grids; instead, we present the theoretical foundations for computing merge trees on CW-complexes, which represent a broader class of meshes. Based on this theoretical foundation, we present two variants of in-situ feature extraction techniques using segmented merge trees. The first approach is a fast, low communication cost technique that generates an exact solution but has limited scalability. The second is a scalable, local approximation that, nevertheless, is guaranteed to correctly extract all features up to a predefined size. We demonstrate both variants using some of the largest combustion simulations available on leadership class supercomputers. Our approach allows feature-based analysis to be performed in-situ at significantly higher frequency than currently possible and with negligible impact on the overall simulation runtime. We provide a detailed performance and scalability analysis of this technique. Furthermore, as scientific applications target exascale, challenges related to power and energy are becoming dominating concerns. To this end, this thesis explores the various performance versus power trade-offs of the presented in-situ technique, studies its behavior when various in-situ computation strategies are employed, and extrapolates the power behavior to peta-scale systems to investigate different design choices through projections.
Type Text
Publisher University of Utah
Subject In-situ analysis techniques; Merge tree computation techniques
Dissertation Name Doctor of Philosophy in Computing
Language eng
Rights Management ©Aaditya G Landge
Format application/pdf
Format Medium application/pdf
Format Extent 4,444,672 bytes
Identifier etd3/id/4253
ARK ark:/87278/s6vt51g4
Setname ir_etd
ID 197798
Reference URL https://collections.lib.utah.edu/ark:/87278/s6vt51g4
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