大规模、树型、自适应网格细化模拟的可视化与分析
Visualization and Analysis of Large-Scale, Tree-Based, Adaptive Mesh Refinement Simulations with Arbitrary Rectilinear Geometry
摘要：
我们在这里提出的第一个系统的处理所带来的问题的可视化和分析的大规模，并行自适应网格细化（AMR）模拟欧拉网格。当与通过构建具有完全描述的连接性的中间非结构化网格所获得的那些相比时，我们的主要结果表明在内存占用方面至少获得80%的增益，具有更好的渲染，同时保持类似的执行速度。在这篇文章中，我们描述的关键概念，使我们能够获得这些结果，以及方法，方便的设计，实施和优化算法直接在这样的细化网格。这种对AMR网格的原生支持已经贡献给了开源可视化工具包（VTK）。这项工作涉及更广泛的长期愿景，其双重目标是在2维和3维探索此类数据集时提高交互性，并优化资源利用率。
英文摘要：
We present here the first systematic treatment of the problems posed by the visualization and analysis of large-scale, parallel adaptive mesh refinement (AMR) simulations on an Eulerian grid. When compared to those obtained by constructing an intermediate unstructured mesh with fully described connectivity, our primary results indicate a gain of at least 80\% in terms of memory footprint, with a better rendering while retaining similar execution speed. In this article, we describe the key concepts that allow us to obtain these results, together with the methodology that facilitates the design, implementation, and optimization of algorithms operating directly on such refined meshes. This native support for AMR meshes has been contributed to the open source Visualization Toolkit (VTK). This work pertains to a broader long-term vision, with the dual goal to both improve interactivity when exploring such data sets in 2 and 3 dimensions, and optimize resource utilization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
