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Summary of the SciTech 2020 Technical Panel on In Situ/In Transit Computational Environments for Visualization and Data Analytics

机译:SCITECH 2020技术小组在原位/在运输计算环境中概述进行可视化和数据分析

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At the AIAA SciTech 2020 conference, the Meshing, Visualization and Computational Environments Technical Committee hosted a special technical panel on In Situ/In Transit Computational Environments for Visualization and Data Analytics. The panel brought together leading experts from industry, software vendors, Department of Energy, Department of Defense and the Japan Aerospace Exploration Agency (JAXA). In situ and in transit methodologies enable Computational Fluid Dynamic (CFD) simulations to avoid the excessive overhead associated with data I/O at large scales especially as simulations scale to millions of processors. These methods either share the data analysis/visualization pipelines with the memory space of the solver or efficiently off load the workload to alternate processors. Using these methods, simulations can scale and have the promise of enabling the community to satisfy the Knowledge Extraction milestones as envisioned by the CFD Vision 2030 study for "on demand analysis/visualization of a 100 Billion point unsteady CFD simulation". This paper summarizes the presentations providing a discussion point of how the community can achieve the goals set forth in the CFD Vision 2030.
机译:在AIAA Scitech 2020会议上,网格化,可视化和计算环境技术委员会在原位/在运输计算环境中举办了一个特殊的技术小组,以进行可视化和数据分析。该小组汇集了行业,软件供应商,国防部和日本航天勘探机构(JAXA)的能源部的领先专家。原位和在运输方法中,能够实现计算流体动态(CFD)模拟,以避免与大尺度的数据I / O相关的过度开销,特别是模拟比例为数百万处理器。这些方法与求解器的存储空间共享数据分析/可视化管道,也可以有效地将工作负载加载到备用处理器。使用这些方法,模拟可以扩展并具有使社区能够满足知识提取里程碑,以满足CFD Vision 2030的研究,以“按需分析/可视化的1000亿点不稳定的CFD仿真”进行研究。本文总结了提供如何讨论社区如何实现CFD视觉2030中规定的目标的讨论点。

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