cuRAMSES: Scalable AMR Optimizations for Large-Scale Cosmological Simulations

Clicks: 2
ID: 316520
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #755 of 908 articles by views in monthly notices of the royal astronomical society

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 908 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract We present cuRAMSES, a suite of advanced domain decomposition strategies and algorithmic optimizations for the ramses adaptive mesh refinement (AMR) code, designed to overcome the communication, memory, and solver bottlenecks inherent in massive cosmological simulations. The central innovation is a recursive k-section domain decomposition that replaces the traditional Hilbert curve ordering with a hierarchical spatial partitioning. This approach substitutes global all-to-all communications with neighbour-only point-to-point communications. By maintaining a constant number of communication partners regardless of the total rank count, it significantly improves strong scaling at high concurrency. To address critical memory constraints at scale, we introduce a Morton-key hash table for octree-neighbour lookup alongside on-demand array allocation, drastically reducing the per-rank memory footprint. Furthermore, a novel spatial hash-binning algorithm in box-type local domains accelerates supernova and AGN feedback routines by over two orders of magnitude (a ~260 × speedup). For hybrid architectures, an automatic CPU/GPU dispatch model with GPU-resident mesh data is implemented and benchmarked. The multigrid Poisson solver achieves a 1.7 × GPU speedup on H100 and A100 GPUs, although the Godunov solver is currently PCIe-bandwidth-limited. The net improvement is ~20 per cent on current PCIe-connected hardware, and a performance model predicts ~2 × on tightly coupled architectures such as the NVIDIA GH200. Additionally, a variable-Nrank restart capability enables flexible I/O workflows. Extensive diagnostics verify that all modifications preserve mass, momentum, and energy conservation, matching the reference Hilbert-ordering run to within 0.5 per cent in the total energy diagnostic.
Reference Key
openalex_W7164035301 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Juhan Kim
Journal monthly notices of the royal astronomical society
Year 2026
DOI
10.1093/mnras/stag1044
URL
https://doi.org/10.1093/mnras/stag1044
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.