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Scientific Computing

Numerical simulation of real-world phenomena provides fertile ground for building interdisciplinary relationships. The SCI Institute has a long tradition of building these relationships in a win-win fashion – a win for the theoretical and algorithmic development of numerical modeling and simulation techniques and a win for the discipline-specific science of interest. High-order and adaptive methods, uncertainty quantification, complexity analysis, and parallelization are just some of the topics being investigated by SCI faculty. These areas of computing are being applied to a wide variety of engineering applications ranging from fluid mechanics and solid mechanics to bioelectricity.

Interactive Rendering

 

irunInteractive rendering of arbitrarily large datasets is a fundamental problem in computer graphics and scientific visualization and a critical capability for many real applications. Interactive visualization of large datasets poses substantial challenges. The visualization pipeline may be broken down into four major stages: retrieval from storage, processing in main memory, rendering in the Graphics Processing Unit (GPU), and display on the screen. The performance of each of these stages is limited by several potential bottlenecks (e.g., disk or network bandwidth, main memory size, GPU triangle throughput, and screen resolution). iRun uses out-of-core data management and speculative visibility prefetching to maintain a working-set of the geometry in memory. Our rendering approach uses GPU-assisted volume rendering with a dynamic set of tetrahedra and uses an out-of-core LOD traversal. Finally, our system is implemented in VTK and allows distributed rendering for high-resolution displays. Using a single commodity PC, our system can render datasets consisting of 14 million tetrahedra while maintaining interactive frame rates.