High Performance and Visualization Cluster for Research in Coupled Computational Steering and Visualization for Large Scale Applications

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Researchers at the University of Maryland plan to build a high-performance computing and visualization cluster taking advantage of synergies afforded by coupling central processing units (CPUs), graphics processing units (GPUs), displays, and storage. The infrastructure will be used to support a broad program of computing research that will revolve around understanding, augmenting, and leveraging the power of heterogeneous vector computing enabled by GPU co-processors. The driving force here is the availability of cheap, powerful, and programmable graphics processing units (GPUs) through their commercialization in interactive 3D graphics applications, including interactive games. The CPU-GPU coupled cluster will enable the pursuit of several new research directions in computing, as well as enable a better understanding and fast solutions to several existing interdisciplinary problems through a visualization-assisted computational steering environment. In addition, it will foster research to cast several problems into a better spot on the price-performance curve.Intellectual Impact: The proposed research that will use this cluster falls into several broad interdisciplinary computing areas. The researchers plan to explore visualization of large datasets and algorithms for parallel rendering. In high-performance scientific computing we plan to develop and analyze efficient algorithms for use with complex systems when uncertainty is included in models. The researchers plan to use the cluster for several applications in computational biology, including computational modeling and visualization of proteins, conformational steering in protein structure prediction, folding, and drug design, large-scale phylogeny visualization, and sequence alignment.The researchers also plan to use the cluster for applications in real-time computer vision, real-time 3D virtual audio, and for efficient compilation of signal processing algorithms.Broader Impact: An important aspect of this research is to ensure a high impact of the cluster towards educational and outreach goals. The investigators plan to enrich their current coursework with research results obtained on the cluster. The coupled cluster with a large-area high-resolution display screen will serve as a valuable resource to present, interactively explore, evaluate, and validate the ongoing research in visualization, vision, scientific computing, human-computer interfaces, and computational biology with active participation of graduate as well as undergraduate students.