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dc.contributor.authorAbrams, Gregoryen_US
dc.contributor.authorAdhinarayanan, Vigneshen_US
dc.contributor.authorFeng, Wu-chunen_US
dc.contributor.authorRogers, Daviden_US
dc.contributor.authorAhrens, Jamsen_US
dc.contributor.authorWilson, Lukeen_US
dc.date.accessioned2017-09-29T18:15:22Z
dc.date.available2017-09-29T18:15:22Z
dc.date.issued2017-09-29
dc.identifier.urihttp://hdl.handle.net/10919/79454
dc.description.abstractAs high-performance computing (HPC) moves towards the exascale era, large-scale scientific simulations are generating enormous datasets. A variety of techniques (e.g., in-situ methods, data sampling, and compression) have been proposed to help visualize these large datasets under various constraints such as storage, power, and energy. However, evaluating these techniques and understanding the various trade-offs (e.g., performance, efficiency, quality) remains a challenging task. To enable the investigation and optimization across such tradeoffs, we propose a toolkit for the early-stage exploration of visualization and rendering approaches, job layout, and visualization pipelines. Our framework covers a broader parameter space than existing visualization applications such as ParaView and VisIt. It also promotes the study of simulation-visualization coupling strategies through a data-centric approach, rather than requiring the code itself. Furthermore, with experimentation on an extensively instrumented supercomputer, we study more metrics of interest than was previously possible. Overall, our framework will help to answer important what-if scenarios and trade-off questions in early stages of pipeline development, helping scientists to make informed choices about how to best couple a simulation code with visualization at extreme scale.en_US
dc.description.sponsorshipThis material is based upon work supported by Dr. Lucy Nowell of the U.S. Department of Energy Office of Science, Advanced Scientific Computing Research under Award Numbers DE-SC0012513, DE-SC0012637, and DE-SC-0012516. We would like to acknowledge Galen Gisler (LANL) for the asteroid simulation and contributors to the hardware/hybrid accelerated cosmology Code (HACC). This work was published under LANL release LA-UR-17-26715.en_US
dc.language.isoen_USen_US
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen_US
dc.relation.ispartofComputer Science Technical Reportsen_US
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectHigh Performance Computingen_US
dc.subjectParallel and Distributed Computingen_US
dc.subjectComputer Systemsen_US
dc.subjectAlgorithmsen_US
dc.subjectComputational Science and Engineeringen_US
dc.subjectModeling and Simulationen_US
dc.titleETH: A Framework for the Design-Space Exploration of Extreme-Scale Visualizationen_US
dc.typeTechnical reporten_US
dc.contributor.departmentComputer Scienceen_US
dc.identifier.trnumberTR-17-05en_US
dc.type.dcmitypeTexten_US


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