DROPS

Document

DOI: 10.4230/LIPIcs.SoCG.2020.70

GPU-Accelerated Computation of Vietoris-Rips Persistence Barcodes

Authors: Simon Zhang, Mengbai Xiao, and Hao Wang

Published in: LIPIcs, Volume 164, 36th International Symposium on Computational Geometry (SoCG 2020)

Abstract

The computation of Vietoris-Rips persistence barcodes is both execution-intensive and memory-intensive. In this paper, we study the computational structure of Vietoris-Rips persistence barcodes, and identify several unique mathematical properties and algorithmic opportunities with connections to the GPU. Mathematically and empirically, we look into the properties of apparent pairs, which are independently identifiable persistence pairs comprising up to 99% of persistence pairs. We give theoretical upper and lower bounds of the apparent pair rate and model the average case. We also design massively parallel algorithms to take advantage of the very large number of simplices that can be processed independently of each other. Having identified these opportunities, we develop a GPU-accelerated software for computing Vietoris-Rips persistence barcodes, called Ripser++. The software achieves up to 30x speedup over the total execution time of the original Ripser and also reduces CPU-memory usage by up to 2.0x. We believe our GPU-acceleration based efforts open a new chapter for the advancement of topological data analysis in the post-Moore’s Law era.

Cite as

Simon Zhang, Mengbai Xiao, and Hao Wang. GPU-Accelerated Computation of Vietoris-Rips Persistence Barcodes. In 36th International Symposium on Computational Geometry (SoCG 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 164, pp. 70:1-70:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

Copy BibTex To Clipboard

@InProceedings{zhang_et_al:LIPIcs.SoCG.2020.70,
  author =	{Zhang, Simon and Xiao, Mengbai and Wang, Hao},
  title =	{{GPU-Accelerated Computation of Vietoris-Rips Persistence Barcodes}},
  booktitle =	{36th International Symposium on Computational Geometry (SoCG 2020)},
  pages =	{70:1--70:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-143-6},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{164},
  editor =	{Cabello, Sergio and Chen, Danny Z.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2020.70},
  URN =		{urn:nbn:de:0030-drops-122287},
  doi =		{10.4230/LIPIcs.SoCG.2020.70},
  annote =	{Keywords: Parallel Algorithms, Topological Data Analysis, Vietoris-Rips, Persistent Homology, Apparent Pairs, High Performance Computing, GPU, Random Graphs}
}

Search Results

Documents authored by Zhang, Simon

GPU-Accelerated Computation of Vietoris-Rips Persistence Barcodes

Abstract

Cite as

Thanks for your feedback!

Could not send message