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FlowPM: Distributed TensorFlow Implementation of the FastPM Cosmological N-body Solver

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arxiv 2010.11847 v1 pith:2LZOD5BX submitted 2020-10-22 astro-ph.CO astro-ph.IM

Chirag Modi , Francois Lanusse , Uros Seljak This is my paper · ORCID
classification astro-ph.COastro-ph.IM
keywords codecosmologicaldistributedflowpmefficientlyforwardlargemodel
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We present FlowPM, a Particle-Mesh (PM) cosmological N-body code implemented in Mesh-TensorFlow for GPU-accelerated, distributed, and differentiable simulations. We implement and validate the accuracy of a novel multi-grid scheme based on multiresolution pyramids to compute large scale forces efficiently on distributed platforms. We explore the scaling of the simulation on large-scale supercomputers and compare it with corresponding python based PM code, finding on an average 10x speed-up in terms of wallclock time. We also demonstrate how this novel tool can be used for efficiently solving large scale cosmological inference problems, in particular reconstruction of cosmological fields in a forward model Bayesian framework with hybrid PM and neural network forward model. We provide skeleton code for these examples and the entire code is publicly available at https://github.com/modichirag/flowpm.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DISCO-DJ II: a differentiable particle-mesh code for cosmology

    astro-ph.CO 2025-10 conditional novelty 6.0 of 10

    A GPU-accelerated, differentiable particle-mesh N-body code achieves per-cent-level power-spectrum accuracy with few time steps and recovers sigma_8 plus initial conditions from a noisy mock field.

  2. Fast Sampling of Cosmological Initial Conditions with Gaussian Neural Posterior Estimation

    astro-ph.CO 2025-02 conditional novelty 6.0 of 10

    Cosmological initial conditions can be sampled from a learned Gaussian posterior with a Fourier-diagonal covariance, giving thousands of reconstructions in seconds on a GPU.

  3. Diagnosing Systematic Effects Using the Inferred Initial Power Spectrum

    astro-ph.CO 2024-12 conditional novelty 6.0 of 10

    The inferred initial matter power spectrum from the SELFI algorithm reveals misspecified galaxy bias, selection, mask, redshift, and gravity models, exposing a >2σ cosmological bias before parameter inference.

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