jFoF: GPU Friends-of-Friends Halo Finding with Gradient Propagation

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ID: 318016
2026
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Abstract
Abstract We present jFoF, a fully GPU-native Friends-of-Friends (FoF) halo finder designed for both high-performance simulation analysis and differentiable modeling. Implemented in JAX, jFoF achieves end-to-end acceleration by performing all neighbor searches, label propagation, and group construction directly on GPUs, eliminating costly host-device transfers. We introduce two complementary neighbor-search strategies, a standard k-d tree and a novel linked-cell grid, and demonstrate that jFoF attains up to an order-of-magnitude speedup compared to optimized CPU implementations while maintaining consistent halo catalogs. Beyond performance, jFoF enables gradient propagation through discrete halo-finding operations via both frozen-assignment and topological optimization modes. Using a topological optimization approach via a REINFORCE-style estimator, our approach allows smooth optimization of halo connectivity and membership, bridging continuous simulation fields with discrete structure catalogs. These capabilities make jFoF a foundation for differentiable inference, enabling end-to-end, gradient-based optimization of structure formation models within GPU-accelerated astrophysical pipelines.
Reference Key
openalex_W7165167966 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Benjamin Horowitz, Adrian E. Bayer
Journal monthly notices of the royal astronomical society
Year 2026
DOI
10.1093/mnras/stag1151
URL
https://doi.org/10.1093/mnras/stag1151
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