gCAMB: A GPU-accelerated Boltzmann solver for next-generation cosmological surveys
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912615256555520 |
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| author | Storchi, L. Campeti, P. Lattanzi, M. Antonini, N. Calore, E. Lubrano, P. |
| author_facet | Storchi, L. Campeti, P. Lattanzi, M. Antonini, N. Calore, E. Lubrano, P. |
| contents | Inferring cosmological parameters from Cosmic Microwave Background (CMB) data requires repeated and computationally expensive calculations of theoretical angular power spectra using Boltzmann solvers like CAMB. This creates a significant bottleneck, particularly for non-standard cosmological models and the high-accuracy demands of future surveys. While emulators based on deep neural networks can accelerate this process by several orders of magnitude, they first require large, pre-computed training datasets, which are costly to generate and model-specific. To address this challenge, we introduce gCAMB, a version of the CAMB code ported to GPUs, which preserves all the features of the original CPU-only code. By offloading the most computationally intensive modules to the GPU, gCAMB significantly accelerates the generation of power spectra, saving massive computational time, halving the power consumption in high-accuracy settings and, among other purposes, facilitating the creation of extensive training sets needed for robust cosmological analyses. We make the gCAMB software available to the community at https://github.com/lstorchi/CAMB/tree/gpuport. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25110 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | gCAMB: A GPU-accelerated Boltzmann solver for next-generation cosmological surveys Storchi, L. Campeti, P. Lattanzi, M. Antonini, N. Calore, E. Lubrano, P. Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Computational Physics Inferring cosmological parameters from Cosmic Microwave Background (CMB) data requires repeated and computationally expensive calculations of theoretical angular power spectra using Boltzmann solvers like CAMB. This creates a significant bottleneck, particularly for non-standard cosmological models and the high-accuracy demands of future surveys. While emulators based on deep neural networks can accelerate this process by several orders of magnitude, they first require large, pre-computed training datasets, which are costly to generate and model-specific. To address this challenge, we introduce gCAMB, a version of the CAMB code ported to GPUs, which preserves all the features of the original CPU-only code. By offloading the most computationally intensive modules to the GPU, gCAMB significantly accelerates the generation of power spectra, saving massive computational time, halving the power consumption in high-accuracy settings and, among other purposes, facilitating the creation of extensive training sets needed for robust cosmological analyses. We make the gCAMB software available to the community at https://github.com/lstorchi/CAMB/tree/gpuport. |
| title | gCAMB: A GPU-accelerated Boltzmann solver for next-generation cosmological surveys |
| topic | Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics Computational Physics |
| url | https://arxiv.org/abs/2509.25110 |