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.
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Authors Benjamin Horowitz, Adrian E. Bayer
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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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