Discrete Morse Sandwich: Fast Computation of Persistence Diagrams for Scalar Data -- An Algorithm and A Benchmark
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Abstract (arXiv)
This paper introduces an efficient algorithm for persistence diagram computation, given an input piecewise linear scalar field f defined on a d-dimensional simplicial complex K, with d ≤ 3. Our work revisits the seminal algorithm "PairSimplices" [31], [103] with discrete Morse theory (DMT) [34], [80], which greatly reduces the number of input simplices to consider. Further, we also extend to DMT and accelerate the stratification strategy described in "PairSimplices" for the fast computation of the 0th and (d - 1)th diagrams, noted D0(f) and Dd-1(f). Minima-saddle persistence pairs (D0(f)) and saddle-maximum persistence pairs (Dd-1(f)) are efficiently computed by processing, with a Union-Find, the unstable sets of 1-saddles and the stable sets of (d - 1)-saddles. This fast pre-computation for the dimensions 0 and (d - 1) enables an aggressive specialization of [4] to the 3D case, which results in a drastic reduction of the number of input simplices for the computation of D1(f), the intermediate layer of the sandwich. Finally, we document several performance improvements via shared-memory parallelism. We provide an open-source implementation of our algorithm for reproducibility purposes. We also contribute a reproducible benchmark package, which exploits three-dimensional data from a public repository and compares our algorithm to a variety of publicly available implementations. Extensive experiments indicate that our algorithm improves by two orders of magnitude the time performance of the seminal "PairSimplices" algorithm it extends. Moreover, it also improves memory footprint and time performance over a selection of 14 competing approaches, with a substantial gain over the fastest available approaches, while producing a strictly identical output.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 21 of its 109 references that are in the map. First posted on arXiv 27 Jun 2022.
Builds on
21 of 109 references.
References · 21
- 2019Ripser: efficient computation of Vietoris-Rips persistence barcodescited by 117
- 2020giotto-tda: A Topological Data Analysis Toolkit for Machine Learning and Data Explorationcited by 55
- 2018The Topology ToolKitcited by 39
- 2020Cubical Ripser: Software for computing persistent homology of image and volume datacited by 23
- 2019Task-based Augmented Contour Trees with Fibonacci Heapscited by 15
- 2019Progressive Wasserstein Barycenters of Persistence Diagramscited by 15
- 2020The Simplex Tree: an Efficient Data Structure for General Simplicial Complexescited by 14
- 2019Ranking Viscous Finger Simulations to an Acquired Ground Truth with Topology-aware Matchingscited by 13
- 2020TopoMap: A 0-dimensional Homology Preserving Projection of High-Dimensional Datacited by 13
- 2013Distributed computation of persistent homologycited by 12
- 2018Persistence Atlas for Critical Point Variability in Ensemblescited by 11
- 2018Lifted Wasserstein Matcher for Fast and Robust Topology Trackingcited by 9
9 more
- 2020Localized Topological Simplification of Scalar Datacited by 9
- 2020Duality in Persistent Homology of Imagescited by 7
- 2018Persistent 1-Cycles: Definition, Computation, and Its Applicationcited by 6
- 2018Topologically Controlled Lossy Compressioncited by 5
- 2017Persistent Homology Guided Force-Directed Graph Layoutscited by 3
- 2013The Compressed Annotation Matrix: an Efficient Data Structure for Computing Persistent Cohomologycited by 2
- 2019Statistical Parameter Selection for Clustering Persistence Diagramscited by 2
- 2020A Progressive Approach to Scalar Field Topologycited by 2
- 2021Fast Approximation of Persistence Diagrams with Guaranteescited by 2
Built on by
Citing papers per year: 2023: 3; 2024: 2; 2025: 4; 2026: 2.
11 papers.
- 2024On the Limitations of Fractal Dimension as a Measure of Generalizationcited by 5
- 2023Wasserstein Dictionaries of Persistence Diagramscited by 4
- 2025Topological Autoencoders++: Fast and Accurate Cycle-Aware Dimensionality Reductioncited by 4
- 2023Wasserstein Auto-Encoders of Merge Trees (and Persistence Diagrams)cited by 3
- 2023TTK is Getting MPI-Readycited by 3
- 2024A Practical Solver for Scalar Data Topological Simplificationcited by 2
- 2025BondMatcher: H-Bond Stability Analysis in Molecular Systemscited by 2
- 2025Topology Aware Neural Interpolation of Scalar Fieldscited by 0
- 2025Continuous Edit Distance, Geodesics and Barycenters of Time-varying Persistence Diagramscited by 0
- 2026DebrisTracer: Reliable Tracking in Hypervelocity Impact Fast Imagingcited by 0
- 2026Sierpi'nski--Knopp Wasserstein Distance for Persistence Diagrams and Applications to 2-Wasserstein Approximationcited by 0
- 2026Sierpi'nski--Knopp Wasserstein Distance for Persistence Diagrams and Applications to 2-Wasserstein Approximation1 Sep 2026
- 2026DebrisTracer: Reliable Tracking in Hypervelocity Impact Fast Imaging17 Jul 2026
- 2025Continuous Edit Distance, Geodesics and Barycenters of Time-varying Persistence Diagrams15 Dec 2025
- 2025Topology Aware Neural Interpolation of Scalar Fields25 Aug 2025
- 2025BondMatcher: H-Bond Stability Analysis in Molecular Systems4 Apr 2025
- 2025Topological Autoencoders++: Fast and Accurate Cycle-Aware Dimensionality Reduction27 Feb 2025
- 2024A Practical Solver for Scalar Data Topological Simplification17 Jul 2024
- 2024On the Limitations of Fractal Dimension as a Measure of Generalization4 Jun 2024
- 2023TTK is Getting MPI-Ready12 Oct 2023
- 2023Wasserstein Auto-Encoders of Merge Trees (and Persistence Diagrams)5 Jul 2023
- 2023Wasserstein Dictionaries of Persistence Diagrams28 Apr 2023