A Survey on the High-Performance Computation of Persistent Homology
Summary
A comprehensive survey of computational approaches for persistent homology, covering algorithmic complexity, distributed computation, memory constraints, and scalability challenges in topological data analysis.
Research Context
This publication reflects graduate research conducted at the University of Cincinnati College of Engineering and Applied Science. It is included here to document the technical foundation behind Convergent Analytics' work in industrial analytics, applied AI, high-performance computing, and topological data analysis.
This peer-reviewed journal article was produced as part of graduate research conducted at the University of Cincinnati College of Engineering and Applied Science.
The paper surveys the computational landscape of persistent homology and topological data analysis, with particular emphasis on scalability challenges encountered in large scientific and engineering datasets. The work evaluates distributed, parallel, and memory-efficient approaches for computing persistent homology and identifies major research directions for future development.
The publication serves as a broad reference for researchers entering the field and provides context for subsequent work involving data reduction, streaming computation, distributed processing, and Euler characteristic methods.
Citation
Malott, N.O., Chen, S., Wilsey, P.A. A Survey on the High-Performance Computation of Persistent Homology. IEEE TKDE. DOI: 10.1109/TKDE.2022.3147070