English

Perfectly parallel cosmological simulations using spatial comoving Lagrangian acceleration

Cosmology and Nongalactic Astrophysics 2022-09-19 v4 Instrumentation and Methods for Astrophysics

Abstract

Existing cosmological simulation methods lack a high degree of parallelism due to the long-range nature of the gravitational force, which limits the size of simulations that can be run at high resolution. To solve this problem, we propose a new, perfectly parallel approach to simulate cosmic structure formation, which is based on the spatial COmoving Lagrangian Acceleration (sCOLA) framework. Building upon a hybrid analytical and numerical description of particles' trajectories, our algorithm allows for an efficient tiling of a cosmological volume, where the dynamics within each tile is computed independently. As a consequence, the degree of parallelism is equal to the number of tiles. We optimised the accuracy of sCOLA through the use of a buffer region around tiles and of appropriate Dirichlet boundary conditions around sCOLA boxes. As a result, we show that cosmological simulations at the degree of accuracy required for the analysis of the next generation of surveys can be run in drastically reduced wall-clock times and with very low memory requirements. The perfect scalability of our algorithm unlocks profoundly new possibilities for computing larger cosmological simulations at high resolution, taking advantage of a variety of hardware architectures.

Keywords

Cite

@article{arxiv.2003.04925,
  title  = {Perfectly parallel cosmological simulations using spatial comoving Lagrangian acceleration},
  author = {Florent Leclercq and Baptiste Faure and Guilhem Lavaux and Benjamin D. Wandelt and Andrew H. Jaffe and Alan F. Heavens and Will J. Percival and Camille Noûs},
  journal= {arXiv preprint arXiv:2003.04925},
  year   = {2022}
}

Comments

24 pages, 8 figures, 2 tables. Typos corrected with respect to A&A published version. The code is publicly available at https://bitbucket.org/florent-leclercq/simbelmyne/

R2 v1 2026-06-23T14:10:39.705Z