论文标题
FTK：一个简单的时空网络框架框架，可用于稳健和可扩展功能跟踪
FTK: A Simplicial Spacetime Meshing Framework for Robust and Scalable Feature Tracking
论文作者
论文摘要
我们介绍了功能跟踪套件（FTK），该框架简化，尺度和提供了各种特征跟踪算法的科学数据。 FTK的关键是我们的高维简单网格划分方案，该方案将常规和非结构化的空间网格推广到时空，同时将时空网格元素插入简单。使用简单的时空网格的好处包括（1）减少歧义案例以提取和跟踪，（2）使用符号扰动来简化脱生的处理，以及（3）启用可扩展和并行处理。简单时空的使用简化并改善了用于关键点，量子涡流和等音表面的几种功能跟踪算法的实现。作为软件框架，FTK为最终用户提供了VTK/Paraview过滤器，Python绑定，命令行界面和用于功能跟踪应用程序的编程接口。我们通过合成数据和科学应用（包括Tokamak，流体动力学和超导性模拟）展示了用例以及可扩展性研究。我们还对峰会超级计算机进行端到端的绩效研究。 FTK根据MIT许可证开源：https：//github.com/hguo/ftk
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
