Andrea Schnorr, M. Sc. |

Dissipation elements (DE) define a geometrical structure for the analysis of small-scale turbulence. Existing analyses based on DEs focus on a statistical treatment of large populations of DEs. In this paper, we propose a method for the interactive visualization of the geometrical shape of DE populations. We follow a two-step approach: in a pre-processing step, we approximate individual DEs by tube-like, implicit shapes with elliptical cross sections of varying radii; we then render these approximations by direct ray-casting thereby avoiding the need for costly generation of detailed, explicit geometry for rasterization. Our results demonstrate that the approximation gives a reasonable representation of DE geometries and the rendering performance is suitable for interactive use.

@InProceedings{Vierjahn2017,

booktitle = {Eurographics Symposium on Parallel Graphics and Visualization},

author = {Tom Vierjahn and Andrea Schnorr and Benjamin Weyers and Dominik Denker and Ingo Wald and Christoph Garth and Torsten W. Kuhlen and Bernd Hentschel},

title = {Interactive Exploration of Dissipation Element Geometry},

year = {2017},

pages = {53--62},

ISSN = {1727-348X},

ISBN = {978-3-03868-034-5},

doi = {10.2312/pgv.20171093},

}

We present an approach for tracking space-filling features based on a two-step algorithm utilizing two graph optimization techniques. First, one-to-one assignments between successive time steps are found by a matching on a weighted, bi-partite graph. Second, events are detected by computing an independent set on potential event explanations. The main objective of this work is investigating options for formal evaluation of complex feature tracking algorithms in the absence of ground truth data.

@INPROCEEDINGS{Schnorr2016, author = {Andrea Schnorr and Sebastian Freitag and Dirk Helmrich and Torsten W. Kuhlen and Bernd Hentschel}, title = {{F}ormal {E}valuation {S}trategies for {F}eature {T}racking}, booktitle = Proc # { the } # LDAV, year = {2016}, pages = {103--104}, abstract = { We present an approach for tracking space-filling features based on a two-step algorithm utilizing two graph optimization techniques. First, one-to-one assignments between successive time steps are found by a matching on a weighted, bi-partite graph. Second, events are detected by computing an independent set on potential event explanations. The main objective of this work is investigating options for formal evaluation of complex feature tracking algorithms in the absence of ground truth data.

}, doi = { 10.1109/LDAV.2016.7874339}}

We present a novel approach for tracking space-filling features, i.e., a set of features covering the entire domain. The assignment between successive time steps is determined by a two-step, global optimization scheme. First, a maximum-weight, maximal matching on a bi-partite graph is computed to provide one-to-one assignments between features of successive time steps. Second, events are detected in a subsequent step; here the matching step serves to restrict the exponentially large set of potential solutions. To this end, we compute an independent set on a graph representing conflicting event explanations. The method is evaluated by tracking dissipation elements, a structure definition from turbulent flow analysis.

**Honorable Mention Award!**

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Eurographics Symposium on Parallel Graphics and Visualization (2015)
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DOI 10.2312/pgv.20151154

Large Data Analysis and Visualization (LDAV), 2015
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DOI 10.1109/LDAV.2015.7348089

@inproceedings {eurp.20161146,

booktitle = {EuroVis 2016 - Posters},

editor = {Tobias Isenberg and Filip Sadlo},

title = {{Tracking Space-Filling Features by Two-Step Optimization}},

author = {Schnorr, Andrea and Freitag, Sebastian and Kuhlen, Torsten W. and Hentschel, Bernd},

year = {2016},

publisher = {The Eurographics Association},

pages = {77--79},

ISBN = {978-3-03868-015-4},

DOI = {10.2312/eurp.20161146}

}

The advection of integral lines is an important computational kernel in vector field visualization. We investigate how this kernel can profit from vector (SIMD) extensions in modern CPUs. As a baseline, we formulate a streamline tracing algorithm that facilitates auto-vectorization by an optimizing compiler. We analyze this algorithm and propose two different optimizations. Our results show that particle tracing does not per se benefit from SIMD computation. Based on a careful analysis of the auto-vectorized code, we propose an optimized data access routine and a re-packing scheme which increases average SIMD efficiency. We evaluate our approach on three different, turbulent flow fields. Our optimized approaches increase integration performance up to 5:6 over our baseline measurement. We conclude with a discussion of current limitations and aspects for future work.

@INPROCEEDINGS{Hentschel2015,

author = {Bernd Hentschel and Jens Henrik G{\"o}bbert and Michael Klemm and

Paul Springer and Andrea Schnorr and Torsten W. Kuhlen},

title = {{P}acket-{O}riented {S}treamline {T}racing on {M}odern {SIMD}

{A}rchitectures},

booktitle = {Proceedings of the Eurographics Symposium on Parallel Graphics

and Visualization},

year = {2015},

pages = {43--52},

abstract = {The advection of integral lines is an important computational

kernel in vector field visualization. We investigate

how this kernel can profit from vector (SIMD) extensions in modern CPUs. As a

baseline, we formulate a streamline

tracing algorithm that facilitates auto-vectorization by an optimizing compiler.

We analyze this algorithm and

propose two different optimizations. Our results show that particle tracing does

not per se benefit from SIMD computation.

Based on a careful analysis of the auto-vectorized code, we propose an optimized

data access routine

and a re-packing scheme which increases average SIMD efficiency. We evaluate our

approach on three different,

turbulent flow fields. Our optimized approaches increase integration performance

up to 5.6x over our baseline

measurement. We conclude with a discussion of current limitations and aspects

for future work.}

}

We present a novel approach for tracking space-filling features, i.e. a set of features which covers the entire domain. In contrast to previous work, we determine the assignment between features from successive time steps by computing a globally optimal, maximum-weight, maximal matching on a weighted, bi-partite graph. We demonstrate the method's functionality by tracking dissipation elements (DEs), a space-filling structure definition from turbulent flow analysis. The ability to track DEs over time enables researchers from fluid mechanics to extend their analysis beyond the assessment of static flow fields to time-dependent settings.

@INPROCEEDINGS{Schnorr2015,

author = {Andrea Schnorr and Jens-Henrik Goebbert and Torsten W. Kuhlen and Bernd Hentschel},

title = {{T}racking {S}pace-{F}illing {S}tructures in {T}urbulent {F}lows},

booktitle = Proc # { the } # LDAV,

year = {2015},

pages = {143--144},

abstract = {We present a novel approach for tracking space-filling features, i.e. a set of features which covers the entire domain. In contrast to previous work, we determine the assignment between features from successive time steps by computing a globally optimal, maximum-weight, maximal matching on a weighted, bi-partite graph. We demonstrate the method's functionality by tracking dissipation elements (DEs), a space-filling structure definition from turbulent flow analysis. The abilitytotrack DEs over time enables researchers from fluid mechanics to extend their analysis beyond the assessment of static flow fields to time-dependent settings.},

doi = {10.1109/LDAV.2015.7348089},

keywords = {Feature Tracking, Weighted, Bi-Partite Matching, Flow

Visualization, Dissipation Elements}

}