gevolution 2.0: GPU-accelerated relativistic N-body simulations for cosmology

arXiv:2607.17929 · astro-ph.IM, astro-ph.CO · Submitted 2026-07-20 · Read on arXiv

Julian Adamek, Øyvind Christiansen

astro-ph.IM, astro-ph.CO

Submitted: 2026-07-20

Comments: 9 pages, 4 figures, 4 tables; code release available at https://github.com/gevolution-code/gevolution-2.0 (gevolution) and https://github.com/gevolution-code/LATfield2 (LATfield2); performance benchmarks available at https://doi.org/10.5281/zenodo.21295696

Code: https://github.com/gevolution-code/gevolution-2.0

License: http://creativecommons.org/licenses/by/4.0/

The gist: High-performance computing is increasingly dominated by hardware acceleration using Graphics Processing Units (GPUs).

Terminology

Abstract

High-performance computing is increasingly dominated by hardware acceleration using Graphics Processing Units (GPUs). To take advantage of this long-term trend, we implement a major overhaul of the parallelisation approach in the relativistic particle-mesh N-body code gevolution. The new version 2.0 of gevolution employs three layers of parallelisation: MPI for scalability on a distributed memory system, shared memory parallelisation on each MPI rank using OpenMP, and offloading all compute intensive tasks to GPUs using CUDA. The code also includes many new features that have been developed over the past years. We provide an overview of the code structure and show key performance benchmarks. The public release of gevolution 2.0 can be found at https://github.com/gevolution-code/gevolution-2.0. We also release a GPU-ready version of the LATfield2 library which provides the parallelisation backend, available at https://github.com/gevolution-code/LATfield2.

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