The study of pulmonary samples from individuals who have died as a direct result of COVID-
Fig. 1. Persistence atlas for an ensemble of 45 von Kármán vortex streets (scalar data: orthogonal component of the curl). (a) Critical points (minima and maxima, scaled by persistence) of a few representative ensemble members (one color per member) exhibit clearly distinct layout patterns in terms of position and number of vortices, revealing high spatial and trend variabilities within the ensemble. (b) Mandatory critical points (minimal regions where at least one critical point is guaranteed to occur for every member of the ensemble) are thus particularly conservative given these variabilities and identify only one region per side of the vortex street (blue: minimum, green: maximum). (c) The persistence atlas addresses this issue by analyzing the structure of the ensemble in terms of critical point layouts and provides low dimensional embeddings of the members where statistical tasks, such as clustering, can be easily carried out. In particular, our approach automatically identified five clusters, (d) to (h), corresponding to five distinct trends in critical point layouts (five viscosity regimes). Per cluster mandatory critical points provide more accurate and useful critical point predictions (colored regions, (d) to (h)), revealing an increasing number of vortices and a decreasing spatial variability for increasing Reynolds numbers (left to right). The background color map shows the mean scalar field for the entire ensemble, (a) and (b), and individual clusters, (d) to (h).Abstract-This paper presents a new approach for the visualization and analysis of the spatial variability of features of interest represented by critical points in ensemble data. Our framework, called Persistence Atlas, enables the visualization of the dominant spatial patterns of critical points, along with statistics regarding their occurrence in the ensemble. The persistence atlas represents in the geometrical domain each dominant pattern in the form of a confidence map for the appearance of critical points. As a by-product, our method also provides 2-dimensional layouts of the entire ensemble, highlighting the main trends at a global level. Our approach is based on the new notion of Persistence Map, a measure of the geometrical density in critical points which leverages the robustness to noise of topological persistence to better emphasize salient features. We show how to leverage spectral embedding to represent the ensemble members as points in a low-dimensional Euclidean space, where distances between points measure the dissimilarities between critical point layouts and where statistical tasks, such as clustering, can be easily carried out. Further, we show how the notion of mandatory critical point can be leveraged to evaluate for each cluster confidence regions for the appearance of critical points. Most of the steps of this framework can be trivially parallelized and we show how to efficiently implement them. Extensive experiments demonstrate the relevance of our approach. The accuracy of the confidence regions provided by the per...
Figure 1: A representative suite of visualization tasks being evaluated with MapReduce: isosurface extraction, volume and mesh rendering, and mesh simplification. Our MapReduce-based renderer can produce a giga pixel rendering of a 1 billion triangle mesh in just under two minutes. With the capability of sustaining high I/O rate with fault-tolerance, MapReduce methods can be used as tools for quickly exploring large datasets with isosurfacing and rendering in a batch-oriented manner. ABSTRACTLarge-scale visualization systems are typically designed to efficiently "push" datasets through the graphics hardware. However, exploratory visualization systems are increasingly expected to support scalable data manipulation, restructuring, and querying capabilities in addition to core visualization algorithms. We posit that new emerging abstractions for parallel data processing, in particular computing clouds, can be leveraged to support large-scale data exploration through visualization. In this paper, we take a first step in evaluating the suitability of the MapReduce framework to implement large-scale visualization techniques. MapReduce is a lightweight, scalable, general-purpose parallel data processing framework increasingly popular in the context of cloud computing. Specifically, we implement and evaluate a representative suite of visualization tasks (mesh rendering, isosurface extraction, and mesh simplification) as MapReduce programs, and report quantitative performance results applying these algorithms to realistic datasets. For example, we perform isosurface extraction of up to l6 isovalues for volumes composed of 27 billion voxels, simplification of meshes with 30GBs of data and subsequent rendering with image resolutions up to 80000 2 pixels. Our results indicate that the parallel scalability, ease of use, ease of access to computing resources, and fault-tolerance of MapReduce offer a promising foundation for a combined data manipulation and data visualization system deployed in a public cloud or a local commodity cluster.
Abstract-The IDX data format provides efficient, cache oblivious, and progressive access to large-scale scientific datasets by storing the data in a hierarchical Z (HZ) order. Data stored in IDX format can be visualized in a i interactive environment allowing for meaningful explorations with minimal required resources. This technology enables real-time, interactive visualization and analysis of large datasets on a variety of systems ranging from desktops and laptop computers to portable devices such as iPhones/iPads and over the web. While the existing ViSUS API for writing IDX data is serial, there are obvious advantages to applying the IDX format to the output of large scale scientific simulations. We have therefore developed PIDX -a parallel API for writing data in an IDX format. With PIDX it is now possible to generate IDX datasets directly from large scale scientific simulations with the added advantage of real-time monitoring and visualization of the generated data.In this paper, we provide an overview of the IDX file format and how it is generated using PIDX. We then present a data model description and a novel aggregation strategy to enhance the scalability of the PIDX library. The S3D combustion application is used as an example to demonstrate the efficacy of PIDX for a real-world scientific simulation. S3D is used for fundamental studies of turbulent combustion requiring exceptionally high fidelity simulations. PIDX achieves up to 18 GiB/s I/O throughput at 8,192 processes for S3D to write data out in the IDX format. This allows for interactive analysis and visualization of S3D data, thus, enabling in situ analysis of S3D simulation.
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