Interactive Exploration and Analysis of Large-Scale Simulations Using Topology-Based Data Segmentation

Abstract

Large-scale simulations are increasingly being used to study complex scientific and engineering phenomena. As a result, advanced visualization and data analysis are also becoming an integral part of the scientific process. Often, a key step in extracting insight from these large simulations involves the definition, extraction, and evaluation of features in the space and time coordinates of the solution. However, in many applications, these features involve a range of parameters and decisions that will affect the quality and direction of the analysis. Examples include particular level sets of a specific scalar field, or local inequalities between derived quantities. A critical step in the analysis is to understand how these arbitrary parameters/decisions impact the statistical properties of the features, since such a characterization will help to evaluate the conclusions of the analysis as a whole. We present a new topological framework that in a single-pass extracts and encodes entire families of possible features definitions as well as their statistical properties. For each time step we construct a hierarchical merge tree a highly compact, yet flexible feature representation. While this data structure is more than two orders of magnitude smaller than the raw simulation data it allows us to extract a set of features for any given parameter selection in a postprocessing step. Furthermore, we augment the trees with additional attributes making it possible to gather a large number of useful global, local, as well as conditional statistic that would otherwise be extremely difficult to compile. We also use this representation to create tracking graphs that describe the temporal evolution of the features over time. Our system provides a linked-view interface to explore the time-evolution of the graph interactively alongside the segmentation, thus making it possible to perform extensive data analysis in a very efficient manner. We demonstrate our framework by extracting and analyzing burning cells from a large-scale turbulent combustion simulation. In particular, we show how the statistical analysis enabled by our techniques provides new insight into the combustion process.

Computing and Visualizing Time-Varying Merge Trees for High-Dimensional Data

“…Once an appropriate fuel consumption threshold is identified, they use the Reeb graph of a 4-D space-time isosurface to track these regions over time [4,28]. In later work, Bremer et al [5] use the merge tree to compute statistics about burning regions in combustion simulations within a single time step. Once an appropriate threshold is identified, burning regions are tracked over time via overlap.…”

“…We display the merge trees as occlusion-free 2-D topological landscape profiles [22], stacked in the third dimension according to their time stamp, and using orthographic projection to facilitate feature comparison. We relate features over time using visual links between the profiles, similar to standard isotracking graphs [25,5], but showing more feature properties and, most importantly, features for all thresholds in the landscape profiles.…”

Combing the Communication Hairball: Visualizing Parallel Execution Traces using Logical Time

“…Here, we analyze a development version of a massively parallel algorithm [27] to compute merge trees, a topological structure that has recently been used to analyze some of the largest combustion simulations [8,5,4]. The algorithm relies on a global gather-scatter approach similar to a V-cycle in a traditional multi-grid solver with the corresponding problem of low parallel efficiency as most processes are kept idle some of the time.…”

“…Fig. 13 shows trace visualizations of both mappings applied to 1,024,8,192, and 32,768 process executions.…”

Topology exploration with hierarchical landscapes

“…Topological information has proven useful in a wide variety of applications ranging from volume rendering [Weber et al 2007b] to combustion analysis [Bremer et al 2011]. The contour tree, in particular, has been used extensively in data analysis and visualization Bajaj et al 1998;van Kreveld et al 1997;Mizuta et al 2004] as it encodes the nesting behavior of all contours of a scalar function.…”