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arXiv Computer Vision By Fan Wang, Hubert Wagner, Rezaul Chowdhury, Chao Chen

GPU-Accelerated Computation of Persistent Homology for Topological Analysis of Image Data

Read the original on arXiv Computer Vision →

The paper presents TopoGPU, a GPU streaming pipeline that computes persistence diagrams of cubical complexes from 2D and 3D images. It processes image chunks with massively parallel GPU kernels, accumulates boundary relations in host memory, and performs boundary matrix reduction on the CPU. TopoGPU introduces a stratification‑aware discrete Morse matching, parallel topological sorting, and a parallel V‑path parity algorithm, achieving up to 198× speedup over Cubical Ripser and reducing persistent homology computation costs in a topology‑preserving deep network.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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