@inproceedings{54e1e2145c2e41c2b956b7e76d0b79fa,
title = "Persistent Homology on GPU for 1d and 2d Cubical Filtrations",
abstract = "This paper describes the algorithmic choices made in a pure GPU implementation to compute persistent homology for cubical complexes in dimension 1 or 2. The main ingredients are the parallel detection of apparent pairs, the simple reduction of the remaining complex when we only consider H0, and the possibility to iterate those two steps. The result can be 200 times faster than a sequential CPU implementation.",
keywords = "GPU, Persistent homology, cubical complex, parallelism",
author = "Marc Glisse",
note = "Publisher Copyright: {\textcopyright} Marc Glisse.; 34th Annual European Symposium on Algorithms, ESA 2026 ; Conference date: 31-08-2026 Through 04-09-2026",
year = "2026",
month = aug,
day = "25",
doi = "10.4230/LIPIcs.ESA.2026.5",
language = "English",
series = "Leibniz International Proceedings in Informatics, LIPIcs",
publisher = "Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing",
editor = "Philip Bille and Seth Pettie and Sabine Storand",
booktitle = "34th Annual European Symposium on Algorithms, ESA 2026",
}