Direct Multifield Volume Ray Casting of Fiber Surfaces

Multifield data are common in visualization. However, reducing these data to comprehensible geometry is a challenging problem. Fiber surfaces, an analogy of isosurfaces to bivariate volume data, are a promising new mechanism for understanding multifield volumes. In this work, we explore direct ray casting of fiber surfaces from volume data without any explicit geometry extraction. We sample directly along rays in domain space, and perform geometric tests in range space where fibers are defined, using a signed distance field derived from the control polygons. Our method requires little preprocess, and enables real-time exploration of data, dynamic modification and pixel-exact rendering of fiber surfaces, and support for higher-order interpolation in domain space. We demonstrate this approach on several bivariate datasets, including analysis of multi-field combustion data

Doctor of Philosophy

dissertationRay tracing presents an efficient rendering algorithm for scientific visualization using common visualization tools and scales with increasingly large geometry counts while allowing for accurate physically-based visualization and analysis, which enables enhanced rendering and new visualization techniques. Interactivity is of great importance for data exploration and analysis in order to gain insight into large-scale data. Increasingly large data sizes are pushing the limits of brute-force rasterization algorithms present in the most widely-used visualization software. Interactive ray tracing presents an alternative rendering solution which scales well on multicore shared memory machines and multinode distributed systems while scaling with increasing geometry counts through logarithmic acceleration structure traversals. Ray tracing within existing tools also provides enhanced rendering options over current implementations, giving users additional insight from better depth cues while also enabling publication-quality rendering and new models of visualization such as replicating photographic visualization techniques

Skeletons for Distributed Topological Computation

Parallel implementation of topological algorithms is highly desirable, but the challenges, from reconstructing algorithms around independent threads through to runtime load balancing, have proven to be formidable. This problem, made all the more acute by the diversity of hardware platforms, has led to new kinds of implementation platform for computational science, with sophisticated runtime systems managing and coordinating large threadcounts to keep processing elements heavily utilized. While simpler and more portable than direct management of threads, these approaches still entangle program logic with resource management. Similar kinds of highly parallel runtime system have also been developed for functional languages. Here, however, language support for higher-order functions allows a cleaner separation between the algorithm and `skeletons' that express generic patterns of parallel computation. We report results on using this technique to develop a distributed version of the Joint Contour Net, a generalization of the Contour Tree to multifields. We present performance comparisons against a recent Haskell implementation using shared-memory parallelism, and initial work on a skeleton for distributed memory implementation that utilizes an innovative strategy to reduce inter-process communication overheads

Multi-cultural visualization : how functional programming can enrich visualization (and vice versa)

The past two decades have seen visualization flourish as a research field in its own right, with advances on the computational challenges of faster algorithms, new techniques for datasets too large for in-core processing, and advances in understanding the perceptual and cognitive processes recruited by visualization systems, and through this, how to improve the representation of data. However, progress within visualization has sometimes proceeded in parallel with that in other branches of computer science, and there is a danger that when novel solutions ossify into `accepted practice' the field can easily overlook significant advances elsewhere in the community. In this paper we describe recent advances in the design and implementation of pure functional programming languages that, significantly, contain important insights into questions raised by the recent NIH/NSF report on Visualization Challenges. We argue and demonstrate that modern functional languages combine high-level mathematically-based specifications of visualization techniques, concise implementation of algorithms through fine-grained composition, support for writing correct programs through strong type checking, and a different kind of modularity inherent in the abstractive power of these languages. And to cap it off, we have initial evidence that in some cases functional implementations are faster than their imperative counterparts