Material interface reconstruction

The paper presents an algorithm for material interface reconstruction for data sets where fractional material information is given as a percentage for each element of the underlying grid. The reconstruction problem is transformed to a problem that analyzes a dual grid, where each vertex in the dual grid has an associated barycentric coordinate tuple that represents the fraction of each material present. Material boundaries are constructed by analyzing the barycentric coordinate tuples of a tetrahedron in material space and calculating intersections with Voronoi cells that represent the regions where one material dominates. These intersections are used to calculate intersections in the Euclidean coordinates of the tetrahedron. By triangulating these intersection points, one creates the material boundary. The algorithm can treat data sets containing any number of materials. The algorithm can also create nonmanifold boundary surfaces if necessary. By clipping the generated material boundaries against the original cells, one can examine the error in the algorithm. Error analysis shows that the algorithm preserves volume fractions within an error range of 0.5 percent per material.     

High-resolution multiprojector display walls

Meeting the demands of the current tera-scale visualization community requires the use of large- and small-scale tiled displays. The Lawrence Livermore National Laboratory (LLNL) efforts in this area have led to the creation of one of the largest interactive tiled displays built to date, and a number of new efforts for “personal” tiled displays. Visualization hardware and software now being built will display up to 15 times the number of pixels in a typical desktop display. We outline the system implemented at LLNL for the creation and support of large, tiled, multi-pipe graphics displays. The system builds on a simple, portable, parallel API for tiling OpenGL commands in parallel to multiple contexts on a set of X servers. This API has allowed us to retrofit existing visualization and analysis codes to support these displays with very little effort. While this work represents a first step toward the ultimate goal of scalable visualization in individual office spaces, the result has been the creation of useful new visualization workspaces and tools for our users     

Distributed and collaborative visualization

Visualization typically involves large computational tasks, often performed on supercomputers. The results of these tasks are usually analyzed by a design team consisting of several members. Our goal is to depart from traditional single-user systems and build a low-cost scientific visualization environment that enables computer-supported cooperative work in the distributed setting. A synchronously conferenced collaborative visualization environment would let multiple users on a network of workstations and supercomputers share large data sets, simultaneously view visualizations of the data, and interact with multiple views while varying parameters. Such an environment would support collaboration in both the problem-solving phase and the review phase of design tasks. In this article we describe two distributed visualization algorithms and the facilities that enable collaborative visualization. These are all implemented on top of the distribution and collaboration mechanisms of an environment called Shastra, executing on a set of low-cost networked workstations