David H. Rogers

An image-based approach to extreme scale in situ visualization and analysis

Extreme scale scientific simulations are leading a charge to exascale computation, and data analytics runs the risk of being a bottleneck to scientific discovery. Due to power and I/O constraints, we expect in situ visualization and analysis will be a critical component of these workflows. Options for extreme scale data analysis are often presented as a stark contrast: write large files to disk for interactive, exploratory analysis, or perform in situ analysis to save detailed data about phenomena that a scientists knows about in advance. We present a novel framework for a third option - a highly interactive, image-based approach that promotes exploration of simulation results, and is easily accessed through extensions to widely used open source tools. This in situ approach supports interactive exploration of a wide range of results, while still significantly reducing data movement and storage.

Evaluation of methods to integrate analysis into a large-scale shock shock physics code

Exascale supercomputing will embody many revolutionary changes in the hardware and software of high-performance computing. For example, projected limitations in power and I/O-system performance will fundamentally change visualization and analysis workflows. A traditional post-processing workflow involves storing simulation results to disk and later retrieving them for visualization and data analysis; however, at Exascale, post-processing approaches will not be able to capture the volume or granularity of data necessary for analysis of these extreme-scale simulations. As an alternative, researchers are exploring ways to integrate analysis and simulation without using the storage system. In situ and in transit are two options, but there has not been an adequate evaluation of these approaches to identify strengths, weaknesses, and trade-offs at large scale. This paper provides a detailed performance and scaling analysis of a large-scale shock physics code using traditional post-processsing, in situ, and in transit analysis to detect material fragments from a simulated explosion.

Cinema image-based in situ analysis and visualization of MPAS-ocean simulations

We created an in situ exploration visualization of an MPAS-Ocean simulation.We leveraged compositing in Cinema to provide interactive exploration.We decreased the storage footprint of the analysis and visualization results. Due to power and I/O constraints associated with extreme scale scientific simulations, in situ analysis and visualization will become a critical component to scientific exploration and discovery. Current analysis and visualization options at extreme scale are presented in opposition: write files to disk for interactive, exploratory analysis, or perform in situ analysis to save data products about phenomena that a scientists knows about in advance. In this paper, we demonstrate extreme scale visualization of MPAS-Ocean simulations leveraging a third option based on Cinema, which is a novel framework for highly interactive, image-based in situ analysis and visualization that promotes exploration.

The Good, the Bad, and the Ugly: A Theoretical Framework for the Assessment of Continuous Colormaps

A myriad of design rules for what constitutes a “good” colormap can be found in the literature. Some common rules include order, uniformity, and high discriminative power. However, the meaning of many of these terms is often ambiguous or open to interpretation. At times, different authors may use the same term to describe different concepts or the same rule is described by varying nomenclature. These ambiguities stand in the way of collaborative work, the design of experiments to assess the characteristics of colormaps, and automated colormap generation. In this paper, we review current and historical guidelines for colormap design. We propose a specified taxonomy and provide unambiguous mathematical definitions for the most common design rules.

Interactive Colormapping: Enabling Multiple Data Range and Detailed Views of Ocean Salinity

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.

The Contribution of Stereoscopic and Motion Depth Cues to the Perception of Structures in 3D Point Clouds

Particle-based simulations are used across many science domains, and it is well known that stereoscopic viewing and kinetic depth enhance our ability to perceive the 3D structure of such data. But the relative advantages of stereo and kinetic depth have not been studied for point cloud data, although they have been studied for 3D networks. This article reports two experiments assessing human ability to perceive 3D structures in point clouds as a function of different viewing parameters. In the first study, the number of discrete views was varied to determine the extent to which smooth motion is needed. Also, half the trials had stereoscopic viewing and half had no stereo. The results showed kinetic depth to be more beneficial than stereo viewing in terms of accuracy and so long as the motion was smooth. The second experiment varied the amplitude of oscillatory motion from 0 to 16 degrees. The results showed an increase in detection rate with amplitude, with the best amplitudes being 4 degrees and greater. Overall, motion was shown to yield greater accuracy, but at the expense of longer response times in comparison with stereoscopic viewing.

Interactive visualization of multi-data-set Rietveld analyses using Cinema:Debye-Scherrer

A tool to visualize the results of a series of Rietveld analyses is presented, allowing identification of analysis problems, prediction of suitable starting values and acceleration of scientific insight from the experimental data.

Characterizing and Modeling Power and Energy for Extreme-Scale In-Situ Visualization

Plans for exascale computing have identified power and energy as looming problems for simulations running at that scale. In particular, writing to disk all the data generated by these simulations is becoming prohibitively expensive due to the energy consumption of the supercomputer while it idles waiting for data to be written to permanent storage. In addition, the power cost of data movement is also steadily increasing. A solution to this problem is to write only a small fraction of the data generated while still maintaining the cognitive fidelity of the visualization. With domain scientists increasingly amenable towards adopting an in-situ framework that can identify and extract valuable data from extremely large simulation results and write them to permanent storage as compact images, a large-scale simulation will commit to disk a reduced dataset of data extracts that will be much smaller than the raw results, resulting in a savings in both power and energy. The goal of this paper is two-fold: (i) to understand the role of in-situ techniques in combating power and energy issues of extreme-scale visualization and (ii) to create a model for performance, power, energy, and storage to facilitate what-if analysis. Our experiments on a specially instrumented, dedicated 150-node cluster show that while it is difficult to achieve power savings in practice using in-situ techniques, applications can achieve significant energy savings due to shorter write times for in-situ visualization. We present a characterization of power and energy for in-situ visualization; an application-aware, architecturespecific methodology for modeling and analysis of such in-situ workflows; and results that uncover indirect power savings in visualization workflows for high-performance computing (HPC).

Employing Color Theory to Visualize Volume-rendered Multivariate Ensembles of Asteroid Impact Simulations

We describe explorations and innovations developed to help scientists understand an ensemble of large scale sim- ulations of asteroid impacts in the ocean. The simulations were run to help scientists determine the characteristics of asteroids that NASA should track, so that communities at risk from impact can be given advanced notice. Of rel- evance to the CHI community are 1) hands-on workflow issues specific to exploring ensembles of large scientific data, 2) innovations in exploring such data ensembles with color, and 3) examples of multidisciplinary collaboration.

Animated versus static views of steady flow patterns

Two experiments were conducted to test the hypothesis that animated representations of vector fields are more effective than common static representations even for steady flow. We compared four flow visualization methods: animated streamlets, animated orthogonal line segments (where short lines were elongated orthogonal to the flow direction but animated in the direction of flow), static equally spaced streamlines, and static arrow grids. The first experiment involved a pattern detection task in which the participant searched for an anomalous flow pattern in a field of similar patterns. The results showed that both the animation methods produced more accurate and faster responses. The second experiment involved mentally tracing an advection path from a central dot in the flow field and marking where the path would cross the boundary of a surrounding circle. For this task the animated streamlets resulted in better performance than the other methods, but the animated orthogonal particles resulted in the worst performance. We conclude with recommendations for the representation of steady flow patterns.