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Tuning the performance of a computational persistent homology package
Author(s) -
Hylton Alan,
HenselmanPetrusek Gregory,
Sang Janche,
Short Robert
Publication year - 2019
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.2678
Subject(s) - persistent homology , computer science , software package , multi core processor , software , profiling (computer programming) , computational science , parallel computing , homology (biology) , topological data analysis , floating point , open source , r package , open source software , computer engineering , programming language , algorithm , biology , biochemistry , gene
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.