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 simplicial spacetime 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 .

中文翻译：

---

FTK：用于稳健且可扩展的特征跟踪的简单时空网格划分框架

我们介绍了特征跟踪工具包 (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 的关键是我们的单纯时空网格划分方案，该方案将规则和非结构化空间网格推广到时空，同时将时空网格元素细分为单纯形。使用简单时空网格的好处包括 (1) 减少特征提取和跟踪的歧义情况，(2) 使用符号扰动简化退化的处理，以及 (3) 实现可扩展和并行处理。单纯时空网格划分的使用简化并改进了关键点、量子涡流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、命令行界面和用于特征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。我们还在 Summit 超级计算机上进行端到端的性能研究。FTK 在 MIT 许可下开源：https://github.com/hguo/ftk .