Tuning the Performance of a Computational Persistent Homology Package

TL;DR

This paper enhances the performance of the Eirene persistent homology software by profiling and parallelizing key functions, demonstrating significant speed improvements on multicore hardware.

Contribution

It introduces novel parallelization techniques and performance optimization methods for the Eirene package, a Julia-based persistent homology tool.

Findings

Performance improvements are significant on multicore systems.

Profiling identified key bottlenecks in the software.

Parallelization of functions accelerates data analysis.

Abstract

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.