HPC-driven computational reproducibility in numerical relativity codes: a use case study with IllinoisGRMHD
Open accessAbstract
Reproducibility of results is a cornerstone of the scientific method. Scientific computing encounters two challenges when aiming for this goal. Firstly, reproducibility should not depend on details of the runtime environment, such as the compiler version or computing environment, so results are verifiable by third-parties. Secondly, different versions of software code executed in the same runtime environment should produce consistent numerical results for physical quantities. In this manuscript, we test the feasibility of reproducing scientific results obtained using the IllinoisGRMHD code that is part of an open-source community software for simulation in relativistic astrophysics, the Einstein Toolkit. We verify that numerical results of simulating a single isolated neutron star with IllinoisGRMHD can be reproduced, and compare them to results reported by the code authors in 2015. We use two different supercomputers: Expanse at SDSC, and Stampede2 at TACC. By compiling the source code archived along with the paper on both Expanse and Stampede2, we find that IllinoisGRMHD reproduces results published in its announcement paper up to errors comparable to round-off level changes in initial data parameters. We also verify that a current version of IllinoisGRMHD reproduces these results once we account for bug fixes which have occurred since the original publication.
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The authors follow a number of best practices to make the result as reproducible as possible.
“We follow a number of best practices to make our result as reproducible as possible (see, e.g., Luo et al. 2023).”
From A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys (2026) · cites this paper as Luo et al. 2023
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The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423 · see citing works
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A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys
Luo, Y., Myers, A. D., Drlica-Wagner, A., Dematties, D., Borchani, S., Valdes, F., Dey, A., Schlegel, D., Zhou, R., & DESI Legacy Imaging Surveys Team (2026) · The Open Journal of Astrophysics · doi:10.33232/001c.158430 · Cited by 1 · see citing works
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- Authors
- Luo, Y., Zhang, Q., Haas, R., Etienne, Z. B., & Allen, G.
- Affiliations
- University of Wyoming (US)
- Digital Research Alliance of Canada (CA)
- University of Illinois Urbana-Champaign (US)
- University of Idaho (US)
- Corresponding author
- Luo, Yufeng
- Venue
- Classical and Quantum Gravity
- Publisher
- IOP Publishing
- Published
- 2023-12-22
- Volume
- 41
- Issue
- 2
- Pages
- 025002
- ISSN
- 0264-9381 (ISSN-L)
- 1361-6382
- OpenAlex
- W4389490903
- Cited by
- 2
- References
- 1
- Citations by year
- 2026: 2
- FWCI
- 0.23
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Classical and Quantum Gravity (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-nd)
- arXiv (Cornell University)
- Funders
- Office of Advanced Cyberinfrastructure (2004879)
- High Energy Physics (DE-SC0019022)
- Topics
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Pulsars and Gravitational Waves Research
- Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
- Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Relativity and Gravitational Theory
- Keywords
- computational reproducibility, numerical relativity, isolated neutron stars, Einstein Toolkit, high-performance computing, rounding errors, bug fixes
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