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  • In Situ and Time

    Abstract: Large-scale HPC simulations with their inherent I/O bottleneck have made in situ visualization an essential approach for data analysis, although the idea of in situ visualization dates back to the era of coprocessing in the 1990s. In situ coupling of analysis and visualization to a live simulation circumvents writing raw data to disk for post-mortem analysis -- an approach that is already inefficient for today's very large simulation codes. Instead, with in situ visualization, data abstracts are generated that provide a much higher level of expressiveness per byte. Therefore, more details can be computed and stored for later analysis, providing more insight than traditional methods. This workshop encouraged talks on methods and workflows that have been used for large-scale parallel visualization, with a particular focus on the in situ case.
  • In Situ Visualization with Temporal Caching

    Abstract: In situ visualization is a technique in which plots and other visual analyses are performed in tandem with numerical simulation processes in order to better utilize HPC machine resources. Especially with unattended exploratory engineering simulation analyses, events may occur during the run, which justify supplemental processing. Sometimes though, when the events do occur, the phenomena of interest includes the physics that precipitated the events and this may be the key insight into understanding the phenomena that is being simulated. In situ temporal caching is the temporary storing of produced data in memory for possible later analysis including time varying visualization. The later analysis and visualization still occurs during the simulation run but not until after the significant events have been detected. In this article, we demonstrate how temporal caching can be used with in-line in situ visualization to reduce simulation run-time while still capturing essential simulation results.
  • Alternative Analysis for Construction Progress Data Spatial Visualization

    Abstract: The U.S. Army Corps of Engineers (USACE) construction projects have multiple stakeholders that collaborate with project delivery team members during the execution of these projects. Many of these stakeholders are located across the U.S., which makes virtual interactions a common communication method for these teams. These interactions often lack spatial visualization, which can add complications to the progress reports provided and how the information is received/interpreted. The visualization of project progress and documents would be invaluable to the stakeholders on critical projects constructed by the USACE. This research was conducted to determine alternatives for migrating Resident Management System (RMS) data into a portal web viewer. This report provides proposed solutions to creating these links in efforts to better harmonize data management and improve project presentation.
  • Summary of the SciTech 2020 Technical Panel on In Situ/In Transit Computational Environments for Visualization and Data Analysis

    Link: http://dx.doi.org/10.21079/11681/40887This paper was originally presented at the American Institute of Aeronautics and Astronautics (AIAA) ScitTech 2020 Technical Panel and published online 4 January 2021. Funding by USACE ERDC under Army Direct funding.Report Number: ERDC/ITL MP-21-10Title: Summary of the SciTech 2020 Technical Panel on In
  • PUBLICATION NOTICE: Water Quality Visualization Tools: A Python Application (1/A)

    Abstract: On May 4, 2016, US District Court Judge Simon ordered the US Army Corps of Engineers and two other Action Agencies to produce a comprehensive Environmental Impacts Statement (EIS) by March 26, 2021. To do this, the Columbia River Systems Operation (CRSO) EIS will evaluate and compare a range of alternatives to offset or minimize any remaining unavoidable impacts. Due to the unique large system model approach, there is a need to quickly develop and analyze water quality model results. Therefore, there was a need for several visualization tools to assist the CRSO EIS team in promptly analyzing the results and creating publication-ready graphics. To create the most accessible desktop application for the CRSO EIS team, the Python programming language was used to quickly create three visualization tools. These three tools are only useful for relatively small data sets. If the team wishes to expand the functionality for larger data sets, it is recommended that model execution and analysis be moved to the supercomputers.