giotto-ph: A Python Library for High-Performance Computation of Persistent Homology of Vietoris-Rips Filtrations
Abstract
We introduce giotto-ph, a high-performance, open-source software package for the computation of Vietoris-Rips barcodes. giotto-ph is based on Morozov and Nigmetov's lockfree (multicore) implementation of Ulrich Bauer's Ripser package. It also contains a re-working of the GUDHI library's implementation of Boissonnat and Pritam's Edge Collapser, which can be used as a pre-processing step to dramatically reduce overall run-times in certain scenarios. Our contribution is twofold: on the one hand, we integrate existing state-of-the-art ideas coherently in a single library and provide Python bindings to the C++ code. On the other hand, we increase parallelization opportunities and improve overall performance by adopting more efficient data structures. Our persistent homology backend establishes a new state of the art, surpassing even GPU-accelerated implementations such as Ripser++ when using as few as 5-10 CPU cores. Furthermore, our implementation of Edge Collapser has fewer software dependencies and improved run-times relative to GUDHI's original implementation.
Keywords
Cite
@article{arxiv.2107.05412,
title = {giotto-ph: A Python Library for High-Performance Computation of Persistent Homology of Vietoris-Rips Filtrations},
author = {Julián Burella Pérez and Sydney Hauke and Umberto Lupo and Matteo Caorsi and Alberto Dassatti},
journal= {arXiv preprint arXiv:2107.05412},
year = {2021}
}
Comments
18 pages, 7 figures