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Manual binding and semi-automated interface generation for efficient simulation backends with user-friendly frontends: A~demonstration for epidemic use cases — arXiv:2610.40343

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.40343
License (as archived): CC0-1.0
Archived: 2026-11-01T02:29:05.993Z
SHA-256 of the archived text: 5f134be2866e9e62e64617b36c35935fc906edb7dee47fe32c63730a7fb8fd42
Stable URL of this page: https://metraipolis.com/documento/dc_2bfa360f747e

How to cite

Manual binding and semi-automated interface generation for efficient simulation backends with user-friendly frontends: A~demonstration for epidemic use cases — arXiv:2610.40343 — arXiv (en), https://arxiv.org/abs/2610.40343 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2bfa360f747e (Metraipolis archive dc_2bfa360f747e, sha256 5f134be2866e9e62e64617b36c35935fc906edb7dee47fe32c63730a7fb8fd42)

BibTeX

@misc{dc2bfa360f747e,
  title = {Manual binding and semi-automated interface generation for efficient simulation backends with user-friendly frontends: A~demonstration for epidemic use cases — arXiv:2610.40343},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.40343},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2bfa360f747e; sha256 5f134be2866e9e62e64617b36c35935fc906edb7dee47fe32c63730a7fb8fd42},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.40343. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Challenges and chances of mathematical software are directly entangled with the programming language used. Depending on the background of developers and users, conflicts between different goals may arise. While several, e.g., scripting languages allow a low barrier and user-friendly entry to mathematical modeling and simulation, corresponding implementations will most likely not allow timely outcomes for detailed simulations. In infectious disease modeling, while several large-scale software frameworks rely on C or C++ implementations, most models are implemented in Python or R. While the latter allow for fast prototyping and more interactive development, it has been shown for several applications that C or C++ implementations outperform plain Python or R implementations by factors between one and three orders of magnitude. In particular, in times of pandemics, the urgent need for swift estimation of the system's dynamics makes the use of large-scale models in these noncompiled languages prohibitive. In order to create synergies and close the gap between scientists from the application and the computational domain, binding libraries allow to execute entire, compiled software frameworks from languages such as Python. However, with iterative model development, extension, or addition of new models, implementing bindings can represent another relevant barrier to developers of the C or C++ backends. In this paper, we show how a large-scale software framework with a multitude of infectious disease models can be efficiently exposed to Python. In addition, we suggest a semi-automated way to generate interface code by analyzing generic C++ code through abstract syntax trees, enabling a modularized approach working with intermediate representations.

Tema degli esploratori: calcolo e infrastrutture (elenco arXiv cs.SE, nuovi lavori del giorno).
Categorie arXiv: Software Engineering (cs.SE).
Pagina del lavoro: https://arxiv.org/abs/2610.40343 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-01T02:12:58.499Z dalla pagina https://arxiv.org/list/cs.SE/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).