Visualize This: Lessons from the Front-lines of High Performance Visualization

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dc.contributor.authorMohammed, Ayaten
dc.contributor.authorPolys, Nicholas F.en
dc.contributor.authorFarrah, Duncanen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2020-04-03T18:15:41Zen
dc.date.available2020-04-03T18:15:41Zen
dc.date.issued2020-04-02en
dc.description.abstractThis paper presents a comprehensive workflow to address two major factors in multivariate multidimensional (MVMD) scientific visualization: the scalability of rendering and the scalability of representation (for perception). Our workflow integrates the metrics of scientific computing and visualization across di fferent STEM domains to deliver perceivable visualizations that meet scientists’ expectations. Our approach attempts to balance the performance of MVMD visualizations using techniques such as sub-sampling, domain decomposition, and parallel rendering. When mapping data to visual form we considered: the nature of the data (dimensionality, type, and distribution), the computing power (serial or parallel), and the rendering power (rendering mechanism, format, and display spectrum). We used HPC clusters to perform remote parallel processing and visualization of large-scale data sets such as 3D point clouds, galaxy catalogs, and airflow simulations. Our workflow brings these considerations into a structured form to guide the decisions of visualization designers who deal with large heterogeneous data sets.en
dc.identifier.trnumberTR-20-01en
dc.identifier.urihttp://hdl.handle.net/10919/97517en
dc.language.isoenen
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen
dc.relation.ispartofComputer Science Technical Reportsen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectVisualizationen
dc.subjectHigh Performance Computingen
dc.titleVisualize This: Lessons from the Front-lines of High Performance Visualizationen
dc.typeTechnical reporten
dc.type.dcmitypeTexten

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