Octavius: A High-Performance Analysis Toolkit for Galaxy Simulations
Abstract
Increased computing power and more efficient codes have allowed cosmological simulations of galaxy formation to grow massively in scale: the analysis of their outputs has consequently become a big data computational challenge. We present Octavius, a high-performance, hybrid-parallel simulation analysis toolkit. Octavius ingests a simulation snapshot with associated halo catalogue, locates galaxies with a 6D friends-of-friends algorithm, then produces an HDF5 catalogue of properties for haloes, subhaloes, and galaxies. The analysis is fast and memory efficient: we compare the performance against the widely used predecessor toolkit, Caesar, and show Octavius delivers order-of-magnitude improvements in speed with a factor of >5 reduction in peak memory. We demonstrate near-linear strong scaling of compute and linear peak memory scaling with the number of halo-assigned particles. Compute times scale with the superlinear computational load of larger haloes; we note the pipeline is presently I/O-limited. The code is written in Python and uses a declarative, plugin-based architecture so as to be easily extensible. Octavius can currently analyse SIMBA, SWIFT-KIARA, SWIFT-EAGLE, SWIFT-COLIBRE, and TNG snapshots; AHF, HBT-HERONS, and SUBFIND halo catalogues are supported. Properties computed include masses, galaxy photometry with dust attenuation, and kinematic, radial, morphological, and virial quantities. Output catalogues are easily accessed through a provided user API. The code is open sourced at https://github.com/jp-duminy/octavius
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BibTeX
@misc{duminy2026octavius,
title = {{Octavius: A High-Performance Analysis Toolkit for Galaxy Simulations}},
author = {Jean-Paul Duminy and Jakub Szpila and Romeel Davé},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.08115},
eprint = {2611.08115},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.08115},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Octavius: A High-Performance Analysis Toolkit for Galaxy Simulations
AU - Jean-Paul Duminy
AU - Jakub Szpila
AU - Romeel Davé
PY - 2026
DA - 2026/11/05
PB - arXiv
DO - 10.48550/arxiv.2611.08115
UR - https://arxiv.org/abs/2611.08115
AB - Increased computing power and more efficient codes have allowed cosmological simulations of galaxy formation to grow massively in scale: the analysis of their outputs has consequently become a big data computational challenge. We present Octavius, a high-performance, hybrid-parallel simulation analysis toolkit. Octavius ingests a simulation snapshot with associated halo catalogue, locates galaxies with a 6D friends-of-friends algorithm, then produces an HDF5 catalogue of properties for haloes, subhaloes, and galaxies. The analysis is fast and memory efficient: we compare the performance against the widely used predecessor toolkit, Caesar, and show Octavius delivers order-of-magnitude improvements in speed with a factor of >5 reduction in peak memory. We demonstrate near-linear strong scaling of compute and linear peak memory scaling with the number of halo-assigned particles. Compute times scale with the superlinear computational load of larger haloes; we note the pipeline is presently I/O-limited. The code is written in Python and uses a declarative, plugin-based architecture so as to be easily extensible. Octavius can currently analyse SIMBA, SWIFT-KIARA, SWIFT-EAGLE, SWIFT-COLIBRE, and TNG snapshots; AHF, HBT-HERONS, and SUBFIND halo catalogues are supported. Properties computed include masses, galaxy photometry with dust attenuation, and kinematic, radial, morphological, and virial quantities. Output catalogues are easily accessed through a provided user API. The code is open sourced at https://github.com/jp-duminy/octavius
ER -