  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.
