Tuning the performance of a computational persistent homology package. (1st February 2019)

• Record Type: Journal Article
• Authors: Hylton, Alan; Henselman‐Petrusek, Gregory; Sang, Janche; Short, Robert
• Is Part Of: Software, practice & experience. Volume 49:Number 5(2019)
• Page Start: 885
• Page End: 905
• Publication Date: 2019-02-01
• Subjects: multicore/manycore computing -- performance optimization -- persistent homology -- profiling; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2678
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: In recent years, persistent homology has become an attractive method for data analysis. It captures topological features, such as connected components, holes, and voids, from point cloud data and summarizes the way in which these features appear and disappear in a filtration sequence. In this project, we focus on improving the performance of Eirene, a computational package for persistent homology. Eirene is a 5000‐line open‐source software library implemented in the dynamic programming language Julia. We use the Julia profiling tools to identify performance bottlenecks and develop novel methods to manage them, including the parallelization of some time‐consuming functions on multicore/manycore hardware. Empirical results show that performance can be greatly improved.