Data is moving beyond the scientific community, flooding communication channels and addressing issues of importance to all aspects of daily life. This highlights the need for rich and expressive data representations to communicate the science on which society rests and must act. However, current visualization techniques often lack the broad visual vocabulary needed to accommodate the explosion in data scale, diversity, and audience perspectives. While previous work has mined artistic and design knowledge for color maps and shape affordances (glyphs) in visualization, line encoding has received little attention. In this paper, we report on an exploration of visual properties that extend the vocabulary of the line, particularly for categorical encoding. We describe the creation of a corpus of lines motivated by artistic practice, Gestalt theory, and design principles, and present initial results from a study of how different visual properties influence how people associate these into sets of similar lines. While very preliminary, the findings suggest that a rich set of line attributes will support both association and categorical hierarchies, as well as provoke further inquiry into how and why line encoding can be more expressive in encoding multivariate, multidimensional data.

We present an extension to OSPRay Studio, Intel’s open-source ray tracing application, to support immersive virtual reality experiences in public exhibition settings. This extension enables one to display a single, coherent 3D virtual environment on tiled display walls and use gesture-based interaction techniques to navigate the environment. Additional mechanisms are provided to configure the application for different display arrangements and integrate other motion-tracking technologies.

Climate modeling is a critical resource that helps scientists understand the processes and ramifications of global climate change. At the Los Alamos National Laboratory, The High-Latitude Application and Testing of Earth System Models (HiLAT) Science Focus Area, in collaboration with the Regional Arctic Systems Model (RASM) use a specialized configuration of the DOE’s Energy Exascale Earth System Model (E3SM) to study the changing Arctic environment. Here we describe the collaborative effort to visualize the interactions between the ocean, sea ice, Arctic cyclones and atmospheric rivers.

Galaxy is a multi-tenant server platform for visualization and analysis of data distributed across many nodes of a supercomputer. It presents an interface through which multiple concurrent clients can connect to perform visualization and analysis tasks on shared data. Galaxy minimizes the memory footprint of visualization and analysis tasks to reduce the impact of analysis on the simulation. It does so by utilizing a visualization renderer that incorporates common visualization techniques directly in the rendering process. The client GUI provides a data-flow programming paradigm that enables users to connect to the Galaxy Multiserver, devise complex multi-step analytics workflows, and visualize results on their desktop.

Visualizing regional-scale landslides is essential to conveying the threat of natural hazards to stakeholders and policymakers. Traditional visualization techniques are restricted to postprocessing a limited subset of simulation data and are not scalable to rendering regional-scale models. In situ visualization is a technique of rendering simulation data in real time, i.e., rendering visuals in tandem while the simulation is running. This study develops a scalable N:M interface architecture to visualize regional-scale landslides. We demonstrate the scalability of the architecture by simulating the long runout of the 2014 Oso landslide using the material point method coupled with the Galaxy ray tracing engine rendering 4.2 million material points as spheres. In situ visualization has an amortized runtime increase of 2% compared to nonvisualized simulations. The developed approach can achieve in situ visualization of regional-scale landslides with minimal impact on the simulation process.

Data physicalizations (3D printed terrain models, anatomical scans, or even abstract data) can naturally engage both the visual and haptic senses in ways that are difficult or impossible to do with traditional planar touch screens and even immersive digital displays. Yet, the rigid 3D physicalizations produced with today's most common 3D printers are fundamentally limited for data exploration and querying tasks that require dynamic input (e.g., touch sensing) and output (e.g., animation), functions that are easily handled with digital displays. We introduce a novel style of hybrid virtual + physical visualization designed specifically to support interactive data exploration tasks. Working toward a "best of both worlds" solution, our approach fuses immersive AR, physical 3D data printouts, and touch sensing through the physicalization. We demonstrate that this solution can support three of the most common spatial data querying interactions used in scientific visualization (streamline seeding, dynamic cutting places, and world-in-miniature visualization). Finally, we present quantitative performance data and describe a first application to exploratory visualization of an actively studied supercomputer climate simulation data with feedback from domain scientists.

As computing capacity increases and data grows in both size and complexity, we are capable of understanding our surroundings with increasing nuance. Visualizing this often-multivariate environmental data presents complex visual scenes to be navigated, parsed, analyzed, and communicated. We draw from both the natural world and artistic color theory to present 1) a new color system, designed to establish an affective connection between big environmental data and its original source material, 2) a tool for extracting these workable palettes from natural imagery, and 3) a selection of pre-made linear colormaps and discrete color sets drawn from natural environments.

High-resolution simulation of global climate physics enables us to model how the climate may change under a variety of future scenarios. Such simulations produce vast amounts of information and dense datasets. If interrogated in tandem, these datasets can provide holistic, vital information on Earth’s many integrated systems by revealing the manifold interrelated properties of the atmosphere, ocean, and polar ice, framed by real-world terrain in three-dimensional space as they vary over time. To accomplish this, climate scientists have joined with computer scientists and an artist to develop techniques enabling scientists to see these relationships. The impact of ocean water properties on Antarctic ice shelves illustrates the benefit of this analysis in understanding land ice melt rates and thus sea-level rise.

As high-performance computing (HPC) moves towards the exascale era, large-scale scientific simulations are generating enormous datasets. Many techniques (e.g., in-situ methods, data sampling, and compression) have been proposed to help visualize these large datasets under various constraints such as storage, power, and energy. However, evaluating these techniques and understanding the trade-offs (e.g., performance, efficiency, and quality) remains a challenging task.To enable exploration of the design space across such trade-offs, we propose the Exploration Test Harness (ETH), an architecture for the early-stage exploration of visualization and rendering approaches, job layout, and visualization pipelines. ETH covers a broader parameter space than current large-scale visualization applications such as ParaView and VisIt. It also promotes the study of simulation-visualization coupling strategies through a data-centric approach, rather than requiring coupling with a specific scientific simulation code. Furthermore, with experimentation on an extensively instrumented supercomputer, we study more metrics of interest than was previously possible. Importantly, ETH will help to answer important what-if scenarios and trade-off questions in the early stages of pipeline development, helping scientists to make informed choices about how to best couple a simulation code with visualization at extreme scale.

Visualizing 3D vector fields is challenging because of occlusion problems and the difficulty of providing depth cues that adequately support the perception of direction of flow lines in 3D space. One of the depth cues that has proven most valuable for the perception of other kinds of 3D data, notably 3D networks and 3D point clouds, is structure‐from‐motion (also called the Kinetic Depth Effect); another powerful depth cue is stereoscopic viewing. We carried out an experiment of the perception of direction for short streamlines passing through a cutting plane. The conditions included viewing with and without structure‐from‐motion and with and without stereoscopic depth. Conditions also include comparing streamtubes to lines. The results show that for this particular task, stereo provided an effective depth cue, but structure‐from‐motion did not. Ringed streamtubes and streamcones provided good 3D direction information, even without stereoscopic viewing. We conclude with guidelines for viewing slices through vector fields.

We, as a society, need artists to help us interpret and explain science, but what does an artist's studio look like when today's science is built upon the language of large, increasingly complex data? This paper presents a data visualization design interface that lifts the barriers for artists to engage with actively studied, 3D multivariate datasets. To accomplish this, the interface must weave together the need for creative artistic processes and the challenging constraints of real-time, data-driven 3D computer graphics. The result is an interface for a technical process, but technical in the way artistic printmaking is technical, not in the sense of computer scripting and programming. Using metaphor, computer graphics algorithms and shader program parameters are reimagined as tools in an artist's printmaking studio. These artistic metaphors and language are merged with a puzzle-piece approach to visual programming and matching iconography. Finally, artists access the interface using a web browser, making it possible to design immersive multivariate data visualizations that can be displayed in VR and AR environments using familiar drawing tablets and touch screens. We report on insights from the interdisciplinary design of the interface and early feedback from artists.

We introduce Artifact-Based Rendering (ABR), a framework of tools, algorithms, and processes that makes it possible to produce real, data-driven 3D scientific visualizations with a visual language derived entirely from colors, lines, textures, and forms created using traditional physical media or found in nature. A theory and process for ABR is presented to address three current needs: (i) designing better visualizations by making it possible for non-programmers to rapidly design and critique many alternative data-to-visual mappings; (ii) expanding the visual vocabulary used in scientific visualizations to depict increasingly complex multivariate data; (iii) bringing a more engaging, natural, and human-relatable handcrafted aesthetic to data visualization. New tools and algorithms to support ABR include front-end applets for constructing artifact-based colormaps, optimizing 3D scanned meshes for use in data visualization, and synthesizing textures from artifacts. These are complemented by an interactive rendering engine with custom algorithms and interfaces that demonstrate multiple new visual styles for depicting point, line, surface, and volume data. A within-the-research-team design study provides early evidence of the shift in visualization design processes that ABR is believed to enable when compared to traditional scientific visualization systems. Qualitative user feedback on applications to climate science and brain imaging support the utility of ABR for scientific discovery and public communication.

This report serves as a record that we have successfully completed work on the proposal Optimizing the Energy Usage and Cognitive Value of Extreme Scale Analysis Approaches, or, more succinctly, ECX. This includes successful completion of all three phases of the project, findings as summarized below, strong publications across the fields represented in the proposal, and a set of artifacts (code, websites, and other documentation) available to the public. In total, the results of this project represent significant findings in each domain, and a toolkit that will allow the community to engage with this work, advancing it and contributing to it in the years ahead. They also form the basis for a successfully funded follow-on project.

Figure 1.Datasets rendered by Galaxy using 64-ray cross-node ambient occlusion shadow sampling: (left) volumetric asteroid impact simulation; (center) geometric limestone karst core sample scan; (right) n-body Cosmic Web dark matter simulation. The long-range ambient-occlusion effects in Asteroid and Cosmic Web cannot be performed by conventional sort-last distributed ray tracers, where rays must stop at local data boundaries.We present Galaxy, a fully asynchronous distributed parallel rendering engine geared towards using full global illumination for large-scale visualization. Galaxy provides performant distributed rendering of complex lighting and material models, particularly those that require ray traversal across nodes. Our design is favorable for tightly-coupled in situ scenarios, where data remains on simulation nodes. By employing asynchronous framebuffer updates and a novel subtractive lighting model, we achieve acceptable image quality from the first ray generation, and improve quality throughout the render epoch. On simulated in situ rendering tasks, Galaxy outperforms the current best-of-breed scientific ray tracer by over 3× for distributed geometric and particle data, while providing expanded rendering capability for global illumination and complex materials.

Recent work in the chemistry of molecular clouds in the early universe has found reactions that may create ancient water molecules that predate our Sun. For these to take place, particular conditions of molecular density and temperature must exist. Water molecules will be destroyed by high temperatures, or when densities are insufficient to shield them from UV rays. If the environment is too cold, the reactions will not take place. Water molecules will only be created and persist in areas of the universe in which these properties are suitable, and will do so at different rates depending on local conditions. In this work we combine a high resolution hydrodynamics simulation of the early universe closely coupled with this hydroxyl and water-producing chemistry model to determine how water molecules would be created and distributed in space and time in the early universe. This work is enabled by the computational power of todays supercomputers and simulation technology. The complexity of the chemistry model is significantly higher than that of simple hydrodynamics, making this a computationally intensive model. Vast difference in scale of the physics involved, from the cosmological scale of the universe through the stellar scale of stars and novae to the molecular scale of chemical reactions requires that adaptive mesh refinement (AMR) techniques be used to provide resolution that varies as demanded by the physics. The visualizations presented herein will show the dynamics of the simulation as it evolves over time.

As high-performance computing (HPC) moves towards the exascale era, large-scale scientific simulations are generating enormous datasets. A variety of techniques (e.g., in-situ methods, data sampling, and compression) have been proposed to help visualize these large datasets under various constraints such as storage, power, and energy. However, evaluating these techniques and understanding the various trade-offs (e.g., performance, efficiency, quality) remains a challenging task. To enable the investigation and optimization across such tradeoffs, we propose a toolkit for the early-stage exploration of visualization and rendering approaches, job layout, and visualization pipelines. Our framework covers a broader parameter space than existing visualization applications such as ParaView and VisIt. It also promotes the study of simulation-visualization coupling strategies through a data-centric approach, rather than requiring the code itself. Furthermore, with experimentation on an extensively instrumented supercomputer, we study more metrics of interest than was previously possible. Overall, our framework will help to answer important what-if scenarios and trade-off questions in early stages of pipeline development, helping scientists to make informed choices about how to best couple a simulation code with visualization at extreme scale.

Since tornado prediction is a critically vital task, the need to understand this complex weather phenomena drives a wide and fascinating range of research. Amy McGovern and her team at the University of Oklahoma are applying data mining techniques to hundreds of simulated storms to identify tornado precursors that could increase tornado warning lead time and prediction accuracy. The visualizations created in this project supported by the Extreme Science and Engineering Discovery Environment, via its Extended Collaborative Support Service (XSEDE ECSS) give the group a novel view of their data, helping them to refine the objects they use for the machine learning and data mining, and letting the scientists visually experience all the storms they want to as well as enabling them to see features that are not visible to the naked eye in nature.

Ocean salinity is a critical component to understanding climate change. Salinity concentrations and temperature drive large ocean currents which in turn drive global weather patterns. Melting ice caps lower salinity at the poles while river deltas bring fresh water into the ocean worldwide. These processes slow ocean currents, changing weather patterns and producing extreme climate events which disproportionally affect those living in poverty. Analysis of salinity presents a unique visualization challenge. Important data are found in narrow data ranges, varying with global location. Changing values of salinity are important in understanding ocean currents, but are difficult to map to colors using traditional tools. Commonly used colormaps may not provide sufficient detail for this data. Current editing tools do not easily enable a scientist to explore the subtleties of salinity. We present a workflow, enabled by an interactive colormap tool that allows a scientist to interactively apply sophisticated colormaps to scalar data. The intuitive and immediate interaction of the scientist with the data is a critical contribution of this work.