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| Autori principali: | , , , , , , , , , , , , |
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| Natura: | Preprint |
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2026
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| Accesso online: | https://arxiv.org/abs/2602.06463 |
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| _version_ | 1866917254390611968 |
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| author | Hong, Wensheng Yang, Xiaohu Li, Junde Wang, Huiyuan Chen, Zhao Zhu, Hong-Ming Li, Qingyang Gu, Yizhou Zhang, Youcai Shi, Feng Han, Jiaxin Yu, Yu Zhai, Zhongxu |
| author_facet | Hong, Wensheng Yang, Xiaohu Li, Junde Wang, Huiyuan Chen, Zhao Zhu, Hong-Ming Li, Qingyang Gu, Yizhou Zhang, Youcai Shi, Feng Han, Jiaxin Yu, Yu Zhai, Zhongxu |
| contents | We present a highly scalable, MPI-parallelized framework for reconstructing the initial cosmic density field, designed to meet the computational demands of next-generation cosmological simulations, particularly the upcoming ELUCID-DESI simulation based on DESI BGS data. Building upon the Hamiltonian Monte Carlo approach and the FastPM solver, our code employs domain decomposition to efficiently distribute memory between nodes. Although communication overhead increases the per-step runtime of the MPI version by roughly a factor of eight relative to the shared-memory implementation, our scaling tests-spanning different particle numbers, core counts, and node layouts-show nearly linear scaling with respect to both the number of particles and the number of CPU cores. Furthermore, to significantly reduce computational costs during the initial burn-in phase, we introduce a novel ``guess'' module that rapidly generates a high-quality initial density field. The results of the simulation test confirm substantial efficiency gains: for $256^3$ particles, 53 steps ($\sim$54 CPU hours) are saved; for $1024^3$, 106 steps ($\sim$7500 CPU hours). The relative gain grows with the number of particles, rendering large-volume reconstructions computationally practical for upcoming surveys, including our planned ELUCID-DESI reconstruction simulation with $8192^3$ particles, with a rough estimation of 720 steps ($\sim$37,000,000 CPU hours). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_06463 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ELUCID-DESI I: A Parallel MPI Implementation of the Initial Condition Solver for Large-Scale Reconstruction Simulations Hong, Wensheng Yang, Xiaohu Li, Junde Wang, Huiyuan Chen, Zhao Zhu, Hong-Ming Li, Qingyang Gu, Yizhou Zhang, Youcai Shi, Feng Han, Jiaxin Yu, Yu Zhai, Zhongxu Astrophysics of Galaxies We present a highly scalable, MPI-parallelized framework for reconstructing the initial cosmic density field, designed to meet the computational demands of next-generation cosmological simulations, particularly the upcoming ELUCID-DESI simulation based on DESI BGS data. Building upon the Hamiltonian Monte Carlo approach and the FastPM solver, our code employs domain decomposition to efficiently distribute memory between nodes. Although communication overhead increases the per-step runtime of the MPI version by roughly a factor of eight relative to the shared-memory implementation, our scaling tests-spanning different particle numbers, core counts, and node layouts-show nearly linear scaling with respect to both the number of particles and the number of CPU cores. Furthermore, to significantly reduce computational costs during the initial burn-in phase, we introduce a novel ``guess'' module that rapidly generates a high-quality initial density field. The results of the simulation test confirm substantial efficiency gains: for $256^3$ particles, 53 steps ($\sim$54 CPU hours) are saved; for $1024^3$, 106 steps ($\sim$7500 CPU hours). The relative gain grows with the number of particles, rendering large-volume reconstructions computationally practical for upcoming surveys, including our planned ELUCID-DESI reconstruction simulation with $8192^3$ particles, with a rough estimation of 720 steps ($\sim$37,000,000 CPU hours). |
| title | ELUCID-DESI I: A Parallel MPI Implementation of the Initial Condition Solver for Large-Scale Reconstruction Simulations |
| topic | Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2602.06463 |