A Survey on the High Performance Computation of Persistent Homology
Nicholas O. Malott, Shangye Chen, Philip A. Wilsey · IEEE Transactions on Knowledge and Data Engineering · 2022
Persistent Homology is a computational method of data mining in the field of Topological Data Analysis. Large-scale data analysis with persistent homology is computationally expensive and memory intensive. The performance of persistent homology has been rigorously studied to optimize data encoding and intermediate data structures for high-performance computation. This paper provides an application-centric survey of the High-Performance Computation of Persistent Homology. Computational topology concepts are reviewed and detailed for a broad data science and engineering audience.