2025/09/29 by Storchi, L., Campeti, P., Lattanzi, M. +3
#Computational Physics (physics.comp-ph) #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM)
paper · doi:10.48550/arxiv.2509.25110
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.