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

TL;DR

FTK introduces a high-dimensional simplicial meshing framework that enhances feature tracking in scientific data by simplifying implementation, reducing ambiguities, and enabling scalable, parallel processing across various applications.

Contribution

The paper presents a novel simplicial spacetime meshing scheme that generalizes spatial meshes for robust, scalable feature tracking in scientific datasets, with an open-source software implementation.

Findings

Improved robustness in feature extraction and tracking.

Enhanced scalability and parallel processing capabilities.

Validated effectiveness across diverse scientific applications.

Abstract

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