No abstract

Abstract-Remote visualization is an enabling technology aiming to resolve the barrier of physical distance. Although many researchers have developed innovative algorithms for remote visualization, previous work has focused little on systematically investigating optimal configurations of remote visualization architectures. In this paper, we study caching and prefetching, an important aspect of such architecture design, in order to optimize the fetch time in a remote visualization system. Unlike a processor cache or Web cache, caching for remote visualization is unique and complex. Through actual experimentation and numerical simulation, we have discovered ways to systematically evaluate and search for optimal configurations of remote visualization caches under various scenarios, such as different network speeds, sizes of data for user requests, prefetch schemes, cache depletion schemes, etc. We have also designed a practical infrastructure software to adaptively optimize the caching architecture of general remote visualization systems, when a different application is started or the network condition varies. The lower bound of achievable latency discovered with our approach can aid the design of remote visualization algorithms and the selection of suitable network layouts for a remote visualization system.

Abstract. Gyrokinetic particle simulations are critical to the study of anomalous energy transport associated with plasma microturbulence in magnetic confinement fusion experiments. The simulations are conducted on massively parallel computers and produce large quantities of particles, variables, and time steps, thus presenting a formidable challenge to data analysis tasks. We present two new visualization techniques for scientists to improve their understanding of the time-varying, multivariate particle data. One technique allows scientists to examine correlations in multivariate particle data with tightly coupled views of the data in both physical space and variable space, and to visually identify and track features of interest. The second technique, built into SCIRun, allows scientists to perform range-based queries over a series of time slices and visualize the resulting particles using glyphs. The ability to navigate the multiple dimensions of the particle data, as well as query individual or a collection of particles, enables scientists to not only validate their simulations but also discover new phenomena in their data.

Fusion promises to provide clean and safe energy, and a considerable amount of research effort is underway to turn this aspiration into reality. This work focuses on a building block for analyzing data produced from the simulation of microturbulence in magnetic confinement fusion devices: the task of efficiently extracting regions of interest. Like many other simulations where a large amount of data are produced, the careful study of "interesting" parts of the data is critical to gain understanding. In this paper, we present an efficient approach for finding these regions of interest. Our approach takes full advantage of the underlying mesh structure in magnetic coordinates to produce a compact representation of the mesh points inside the regions and an efficient connected component labeling algorithm for constructing regions from points. This approach scales linearly with the surface area of the regions of interest instead of the volume as shown with both computational complexity analysis and experimental measurements. Furthermore, this new approach is 100s of times faster than a recently published method based on Cartesian coordinates.

Fig. 1. Here, water particles are shown moving through a semi-porous medium of soil grains. The color along the trajectories represent speed. The left side uses our locality-based visualization to provide proximity information and focus+context for a single trajectory. The right side uses standard semi-transparent surface rendering.Abstract-In flow simulations the behavior and properties of particle trajectories often depend on the physical geometry contained in the simulated environment. Understanding the flow in and around the geometry itself is an important part of analyzing the data. Previous work has often utilized focus+context rendering techniques, with an emphasis on showing trajectories while simplifying or illustratively rendering the physical areas. Our research instead emphasizes the local relationship between particle paths and geometry by using a projected multi-field visualization technique. The correlation between a particle path and its surrounding area is calculated on-the-fly and displayed in a non-intrusive manner. In addition, we support visual exploration and comparative analysis through the use of linked information visualization, such as manipulatable curve plots and one-on-one similarity plots. Our technique is demonstrated on particle trajectories from a groundwater simulation and a computer room airflow simulation, where the flow of particles is highly influenced by the dense geometry.