In-Situ Feature Extraction of Large Scale Combustion Simulations Using Segmented Merge Trees

Landge, Aaditya G.; Pascucci, Valerio; Gyulassy, Attila; Bennett, Janine Camille; Kolla, Hemanth; Chen, Jacqueline; Bremer, Peer-Timo

doi:10.1109/sc.2014.88

Cited by 55 publications

(38 citation statements)

References 32 publications

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“…For example, a merge tree records join events between level set components as the threshold is increased, and has been shown to provide a good basis for feature segmentation [LPG*14]. Using this information, data can be stored in a reduced form, yielding substantial reduction in some applications [BKL*11, BWT*11, LPG*14]. Furthermore, the reduced data still permits a flexible amount of post hoc exploration.…”

Section: Derived Representationsmentioning

confidence: 99%

Data Reduction Techniques for Simulation, Visualization and Data Analysis

Marsaglia

Garth

et al. 2018

Computer Graphics Forum

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Data reduction is increasingly being applied to scientific data for numerical simulations, scientific visualizations and data analyses. It is most often used to lower I/O and storage costs, and sometimes to lower in‐memory data size as well. With this paper, we consider five categories of data reduction techniques based on their information loss: (1) truly lossless, (2) near lossless, (3) lossy, (4) mesh reduction and (5) derived representations. We then survey available techniques in each of these categories, summarize their properties from a practical point of view and discuss relative merits within a category. We believe, in total, this work will enable simulation scientists and visualization/data analysis scientists to decide which data reduction techniques will be most helpful for their needs.

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Section: Derived Representationsmentioning

confidence: 99%

Data Reduction Techniques for Simulation, Visualization and Data Analysis

Marsaglia

Garth

et al. 2018

Computer Graphics Forum

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“…Similarly, Landge et al [14] introduce segmented merge trees for segmenting data and identifying threshold-based features. Their approach constructs local merge trees and corrects them based on neighbouring domains.…”

Section: Scaling Sweep and Mergementioning

confidence: 99%

Parallel peak pruning for scalable SMP contour tree computation

Carr

Weber

Sewell

et al. 2016

2016 IEEE 6th Symposium on Large Data Analysis and Visualization (LDAV)

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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 lgt) 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.

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“…A paper simultaneously communicated by Langde et al [18] describes a distributed algorithm for computing join and split trees. Local trees are built for each sub-domain together with a boundary tree for each sub-domain.…”

Section: Related Workmentioning

confidence: 99%

A parallel and memory efficient algorithm for constructing the contour tree

Acharya

Natarajan

2015

2015 IEEE Pacific Visualization Symposium (PacificVis)

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The contour tree is a topological structure associated with a scalar function that tracks the connectivity of the evolving level sets of the function. It supports intuitive and interactive visual exploration and analysis of the scalar function. This paper describes a fast, parallel, and memory efficient algorithm for constructing the contour tree of a scalar function on shared memory systems. Comparisons with existing implementations show significant improvement in both the running time and the memory expended. The proposed algorithm is particularly suited for large datasets that do not fit in memory. For example, the contour tree for a scalar function defined on a 8.6 billion vertex domain (2048 × 2048 × 2048 volume data) can be efficiently constructed using less than 10GB of memory.

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In-Situ Feature Extraction of Large Scale Combustion Simulations Using Segmented Merge Trees

Cited by 55 publications

References 32 publications

Data Reduction Techniques for Simulation, Visualization and Data Analysis

Data Reduction Techniques for Simulation, Visualization and Data Analysis

Parallel peak pruning for scalable SMP contour tree computation

A parallel and memory efficient algorithm for constructing the contour tree

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