HPC enabled immersive and non-immersive visualization of large scale scientific data

Simon Su; Vincent Perry; Michael An; Luis Bravo; Venkat Dasari

doi:10.1117/12.2519315

10 May 2019 HPC enabled immersive and non-immersive visualization of large scale scientific data

Simon Su, Vincent Perry, Michael An, Luis Bravo, Venkat Dasari

Proceedings Volume 11013, Disruptive Technologies in Information Sciences II; 110130P (2019) https://doi.org/10.1117/12.2519315
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Advancement in the areas of high performance computing and computational sciences have facilitated the generation of an enormous amount of research data by computational scientists - the volume, velocity and variability of Big 'Research' Data has increased across all disciplines. An immersive and non-immersive analytics platform capable of handling extreme-scale scientific data will enable scientists to visualize unwieldy simulation data in an intuitive manner and guide the development of sophisticated and targeted analytics to obtain usable information. Our immersive and non-immersive visualization work is an attempt to provide computational scientists with the ability to analyze the extreme-scale data generated. The main purpose of this paper is to identify different characteristics of a scientific data analysis process to provide a general outline for the scientists to select the appropriate visualization systems to perform their data analytics. In addition, we will include some of the details on how to how the immersive and non-immersive visualization hardware and software are setup. We are confident that the findings in our paper will provide scientists with a streamlined and optimal visual analytics workflow.

Conference Presentation

Citation Download Citation

Simon Su, Vincent Perry, Michael An, Luis Bravo, and Venkat Dasari "HPC enabled immersive and non-immersive visualization of large scale scientific data", Proc. SPIE 11013, Disruptive Technologies in Information Sciences II, 110130P (10 May 2019); https://doi.org/10.1117/12.2519315

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