Production-quality Tools for Adaptive Mesh RefinementVisualization

Adaptive Mesh Refinement (AMR) is a highly effectivesimulation method for spanning a large range of spatiotemporal scales,such as astrophysical simulations that must accommodate ranges frominterstellar to sub-planetary. Most mainstream visualization tools stilllack support for AMR as a first class data type and AMR code teams usecustom built applications for AMR visualization. The Department ofEnergy's (DOE's) Science Discovery through Advanced Computing (SciDAC)Visualization and Analytics Center for Enabling Technologies (VACET) isextending and deploying VisIt, an open source visualization tool thataccommodates AMR as a first-class data type, for use asproduction-quality, parallel-capable AMR visual data analysisinfrastructure. This effort will help science teams that use AMR-basedsimulations and who develop their own AMR visual data analysis softwareto realize cost and labor savings

Occam's Razor and Petascale Visual Data Analysis

One of the central challenges facing visualization research is how to effectively enable knowledge discovery. An effective approach will likely combine application architectures that are capable of running on today?s largest platforms to address the challenges posed by large data with visual data analysis techniques that help find, represent, and effectively convey scientifically interesting features and phenomena

“Don’t You Know I Own the Road?” The Link Between Narcissism and Aggressive Driving

Aggressive drivers can make driving dangerous. Over 50% of traffic fatalities are caused by aggressive driving. Aggressive motorists make driving very dangerous. This research tests whether narcissists are more aggressive drivers than other individuals. Narcissists think they are special people who deserve special treatment. When they don’t get the special treatment they think they deserve, narcissists often lash out at others in an aggressive manner. Narcissists might think they “own the road” and can drive anyway they want, and that other drivers should get out of their way. In the article, we conduct three studies to test the link between narcissism and aggressive driving. In Studies 1 (N=139) and 2 (N=100), Luxembourgish motorists completed a measure of narcissism and a self-report measure of aggressive driving. In Study 3 (N=60), American university students completed a measure of narcissism and then completed a driving simulation scenario that contained a number of frustrating elements. Several measures of aggressive driving and road rage were obtained. In all three studies, narcissism was positively related to aggressive driving. A meta-analysis found an average correlation of r=.35 across the three studies. This research replicates previous research linking narcissism to aggression, and extends it to a driving context

Extreme Scaling of Production Visualization Software on Diverse Architectures

We present the results of a series of experiments studying how visualization software scales to massive data sets. Although several paradigms exist for processing large data, we focus on pure parallelism, the dominant approach for production software. These experiments utilized multiple visualization algorithms and were run on multiple architectures. Two types of experiments were performed. For the first, we examined performance at massive scale: 16,000 or more cores and one trillion or more cells. For the second, we studied weak scaling performance. These experiments were performed on the largest data set sizes published to date in visualization literature, and the findings on scaling characteristics and bottlenecks contribute to understanding of how pure parallelism will perform at high levels of concurrency and with very large data sets

The SENSEI Generic In Situ Interface:

The SENSEI generic in situ interface is an API that promotes code portability and reusability. From the simulation view, a developer can instrument their code with the SENSEI API and then make make use of any number of in situ infrastructures. From the method view, a developer can write an in situ method using the SENSEI API, then expect it to run in any number of in situ infrastructures, or be invoked directly from a simulation code, with little or no modification. This paper presents the design principles underlying the SENSEI generic interface, along with some simplified coding examples

A System for Query Based Analysis and Visualization

Today scientists are producing large volumes of data that they wish to explore and visualize. In this paper we describe a system that combines range-based queries with fast lookup to allow a scientist to quickly and efficiently ask "what if?" questions. Unique to our system is the ability to perform "cumulative queries" that work on both an intra- and inter-time step basis. The results of such queries are visualized as frequency histograms and are the input for secondary queries, the results of which are then visualized