FTK：一种用于鲁棒可扩展特征跟踪的简单时空网格框架
FTK: A Simplicial Spacetime Meshing Framework for Robust and Scalable Feature Tracking
摘要：
我们提出了特征跟踪工具包（FTK），这是一个简化，扩展和提供各种科学数据特征跟踪算法的框架。FTK的关键是我们的高维单纯形网格计划，推广规则和非结构化的空间网格的时空，而镶嵌时空网格元素成单纯形。使用单纯时空网格的好处包括：（1）减少特征提取和跟踪的模糊情况，（2）使用符号扰动简化退化处理，以及（3）实现可扩展和并行处理。单纯时空网格的使用简化和改进了临界点、量子涡旋和等值面的几种特征跟踪算法的实现。作为一个软件框架，FTK为最终用户提供了VTK/ParaView过滤器、Python绑定、命令行界面和功能跟踪应用程序的编程界面。我们通过合成数据和科学应用（包括托卡马克、流体动力学和超导模拟）展示用例和可扩展性研究。我们还对Summit超级计算机进行端到端性能研究。FTK在MIT许可下开源：https://github.com/hguo/ftk
英文摘要：
We present the Feature Tracking Kit (FTK), a framework that simplifies, scales, and delivers various feature-tracking algorithms for scientific data. The key of FTK is our high-dimensional simplicial meshing scheme that generalizes both regular and unstructured spatial meshes to spacetime while tessellating spacetime mesh elements into simplices. The benefits of using simplicial spacetime meshes include (1) reducing ambiguity cases for feature extraction and tracking, (2) simplifying the handling of degeneracies using symbolic perturbations, and (3) enabling scalable and parallel processing. The use of simplicial spacetime meshing simplifies and improves the implementation of several feature-tracking algorithms for critical points, quantum vortices, and isosurfaces. As a software framework, FTK provides end users with VTK/ParaView filters, Python bindings, a command line interface, and programming interfaces for feature-tracking applications. We demonstrate use cases as well as scalability studies through both synthetic data and scientific applications including Tokamak, fluid dynamics, and superconductivity simulations. We also conduct end-to-end performance studies on the Summit supercomputer. FTK is open-sourced under the MIT license: https://github.com/hguo/ftk
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