FTK: A Simplicial Spacetime Meshing Framework for Robust and Scalable Feature Tracking

Hanqi Guo, David Lenz, Jiayi Xu, Xin Liang, Wenbin He, Iulian Grindeanu, Han-Wei Shen, Tom Peterka, Todd Munson, Ian Foster

View presentation:2021-10-28T13:00:00ZGMT-0600Change your timezone on the schedule page
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Direct link to video on YouTube: https://youtu.be/K2evrAG9F4M


Feature tracking, spacetime meshing, distributed and parallel processing, critical points, isosurfaces, vortices.


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.