Lessons Learned from Large Data Visualization Software Development for the K computer

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dc.contributor.authorNonaka, Jorjien_US
dc.contributor.authorSakamoto, Naohisaen_US
dc.contributor.editorGillmann, Christina and Krone, Michael and Reina, Guido and Wischgoll, Thomasen_US
dc.date.accessioned2020-05-24T13:35:11Z
dc.date.available2020-05-24T13:35:11Z
dc.date.issued2020
dc.description.abstractHigh Performance Computing (HPC) always had a close relationship with visualization as we can remember the landmark report on ''Visualization in Scientific Computing'', which was credited to have coined the term Scientific Visualization (SciVis). K computer, a Japanese flagship HPC system, appeared in 2011 as the most powerful supercomputer in the Top500 list, and as other similar HPC systems in that ranking, it was designed to enable ''Grand Challenge'' scientific computing with unprecedented scale and size. RIKEN Center for Computational Science (RIKEN R-CCS) operated and provided the K computer's computational resources to the HPC community for almost 8 years until it was decommissioned in 2019. Considering that most of the scientific computing results were publicly presented in the form of visual images and movies, we can infer that the SciVis was widely applied for assisting the domain scientists with their end-to-end scientific computing workflows. In addition to the traditional visualization applications, various others large data visualization software development were conducted in order to tackle the increased size and amount of the simulation outputs. RIKEN R-CCS participated in some of these development and deployment dealing with several environmental and human factors. Although we have no precise statistics regarding the visualization software usage, in this paper, we would like to present some findings and lessons learned from the large data visualization software development in the K computer environment.en_US
dc.description.sectionheadersClosing
dc.description.seriesinformationVisGap - The Gap between Visualization Research and Visualization Software
dc.identifier.doi10.2312/visgap.20201113
dc.identifier.isbn978-3-03868-125-0
dc.identifier.pages77-81
dc.identifier.urihttps://doi.org/10.2312/visgap.20201113
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/visgap20201113
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectHuman centered computing
dc.subjectVisualization systems and tools
dc.subjectApplied computing
dc.subjectPhysical sciences and engineering
dc.subjectComputing methodologies
dc.subjectParallel computing methodologies"
dc.titleLessons Learned from Large Data Visualization Software Development for the K computeren_US
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VisGap2020 - The Gap between Visualization Research and Visualization Software