Major advancements in computational and sensor hardware have enormously facilitated the generation and collection of research data by scientists - the volume, velocity and variety of Big ’Research’ Data has increased across all disciplines. A visual analytics platform capable of handling extreme-scale data will enable scientists to visualize unwieldy data in an intuitive manner and guide the development of sophisticated and targeted analytics to obtain useable information. Reconfigurable Visual Computing Architecture is an attempt to provide scientists with the ability to analyze the extreme-scale data collected. Reconfigurable Visual Computing Architecture requires the research and development of new interdisciplinary technological tools that integrate data, realtime predictive analytics, visualization, and acceleration on heterogeneous computing platforms. Reconfigurable Visual Computing Architecture will provide scientists with a streamlined visual analytics tool.
Simon Su, J. Michael Barton, Michael An, Vincent Perry, Brian Panneton, Luis Bravo, Rajgopal Kannan, and Venkateswara Dasari, "Reconfigurable visual computing architecture for extreme-scale visual analytics," Proc. SPIE 10652, Disruptive Technologies in Information Sciences, 106520M (Presented at SPIE Defense + Security: April 18, 2018; Published: 9 May 2018); https://doi.org/10.1117/12.2303887.
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