EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 337 (2025) EPJ Web Conf., 337 (2025) 01266 References
Open Access
Issue
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
Article Number 01266
Number of page(s) 13
DOI https://doi.org/10.1051/epjconf/202533701266
Published online 07 October 2025
  1. S. Malik, K. Lieret, P. Elmer, M. Hernandez Villanueva, S. Roiser, Train to Sustain, in European Physical Journal Web of Conferences (2024), Vol. 295, p. 05023 [Google Scholar]
  2. ATLAS Collaboration (2024), https://cds.cern.ch/record/2904204 [Google Scholar]
  3. HEP Software Foundation, A Roadmap for HEP Software and Computing R&D for the 2020s, Computing and Software for Big Science 3 (2019), https://doi.org/10.1007/s41781-018-0018-8 [Google Scholar]
  4. J. Pivarski, History and Adoption of Programming Languages in NHEP (2022), https://indico.jlab.org/event/505/contributions/9207/ [Google Scholar]
  5. J. Zoll, R. Brun, J. Shiers, S. Banerjee, O. Schaile, B.a. Holl, ZEBRA : Overview of the ZEBRA System (1995), https://cds.cern.ch/record/2296399 [Google Scholar]
  6. J. Eschle, et al., Potential of the Julia programming language for high energy physics computing (2023), 2306.03675, https://doi.org/10.48550/arXiv.2306.03675 [Google Scholar]
  7. J. Bezanson, A. Edelman, S. Karpinski, V.B. Shah, Julia: A Fresh Approach to Numerical Computing, SIAM Review 59, 65 (2017), https://doi.org/10.1137/141000671 [CrossRef] [Google Scholar]
  8. J. Bezanson, J. Chen, B. Chung, S. Karpinski, V.B. Shah, J. Vitek, L. Zoubritzky, Julia: Dynamism and Performance Reconciled by Design, Proc. ACM Program. Lang. 2 (2018), https://doi.org/10.1145/3276490 [Google Scholar]
  9. T. Besard, C. Foket, B. De Sutter, Effective Extensible Programming: Unleashing Julia on GPUs, IEEE Transactions on Parallel and Distributed Systems (2018), 1712.03112, https://doi.org/10.1109/TPDS.2018.2872064 [Google Scholar]
  10. J. Samaroo, et al. (2025), https://doi.org/10.5281/zenodo.14826765 [Google Scholar]
  11. T. Besard, V. Churavy, A. Edelman, B.D. Sutter, Rapid software prototyping for heterogeneous and distributed platforms, Advances in Engineering Software 132, 29 (2019), https://doi.org/10.1016/j.advengsoft.2019.02.002 [Google Scholar]
  12. T. Besard, C. Foket, B. De Sutter, Effective Extensible Programming: Unleashing Julia on GPUs, IEEE Transactions on Parallel and Distributed Systems 30, 827 (2019) [Google Scholar]
  13. M. Innes, Flux: Elegant Machine Learning with Julia, Journal of Open Source Software (2018), https://doi.org/10.21105/joss.00602 [Google Scholar]
  14. A. Ramadhan, G.L. Wagner, C. Hill, C. Jean-Michel, V. Churavy, A. Souza, A. Edelman, R. Ferrari, J. Marshall, Oceananigans.jl: Fast and friendly geophysical fluid dynamics on GPUs, Journal of Open Source Software 5, 2018 (2020) [Google Scholar]
  15. S. Hunold, S. Steiner, Benchmarking Julia’s communication performance: Is Julia HPC ready or Full HPC?, in 2020 IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS) (IEEE, 2020) [Google Scholar]
  16. S. Byrne, L.C. Wilcox, V. Churavy, MPI. jl: Julia bindings for the Message Passing Interface, in Proceedings of the JuliaCon Conferences (2021), Vol. 1, p. 68 [Google Scholar]
  17. M. Giordano, M. Klöwer, V. Churavy, Productivity meets performance: Julia on A64FX, in 2022 IEEE Itnl. Conf. on Cluster Comp. (CLUSTER) (IEEE, 2022) [Google Scholar]
  18. J.M. Teichgräber, Julia: A competitive high-level choice for performance portability in HPC?, in Seminar (Performance) Portable Programming of HPC Applications (2022) [Google Scholar]
  19. W.F. Godoy et al., Evaluating performance and portability of high-level programming models: Julia, Python/Numba, and Kokkos on exascale nodes, in 2023 IEEE Int. Parallel and Distributed Processing Sym. Workshops (IPDPSW) (IEEE, 2023) [Google Scholar]
  20. J. Regier et al., Cataloging the Visible Universe Through Bayesian Inference at Petascale, in 2018 IEEE Intl. Parallel and Distributed Processing Symposium (2018) [Google Scholar]
  21. J. Regier et al., Approximate inference for constructing astronomical catalogs from images, The Annals of Applied Statistics 13, 1884 (2019), https://doi.org/10.1214/19-AOAS1258 [Google Scholar]
  22. Tech. rep., CERN, Geneva (2022), https://cds.cern.ch/record/2802918 [Google Scholar]
  23. CMS Offline Software and Computing, Tech. rep., CERN, Geneva (2022), https://cds.cern.ch/record/2815292 [Google Scholar]
  24. A. Valassi et al., Challenges in Monte Carlo Event Generator Software for High-Luminosity LHC, Comp. and Software for Big Science 5, 12 (2021), https://doi.org/10.1007/s41781-021-00055-1 [Google Scholar]
  25. Wang et al., AUGEM: Automatically generate high performance Dense Linear Algebra kernels on x86 CPUs, in SC ’13 (2013), pp. 1–12 [Google Scholar]
  26. J.M. Perkel, Julia: come for the syntax, stay for the speed, Nature 572, 141 (2019), https://doi.org/10.1038/d41586-019-02310-3 [CrossRef] [PubMed] [Google Scholar]
  27. I. Antcheva et al., ROOT: A C++ framework for petabyte data storage, statistical analysis and visualization, Comput. Phys. Commun. 182, 1384 (2011) [Google Scholar]
  28. J. Pata, O. Schulz, P. Gras, Julia interface to the C++ ROOT (2025), https://github.com/JuliaHEP/ROOT.jl [Google Scholar]
  29. T. Gál, J.J. Ling, N. Amin, UnROOT: an I/O library for the CERN ROOT file format written in Julia, J. Open Source Softw. 7, 4452 (2022) [Google Scholar]
  30. F. Gaede, T. Madlener, P. Declara Fernandez, G. Ganis, B. Hegner, C. Helsens, A. Sailer, G. A. Stewart, V. Voelkl, EDM4hep - a common event data model for HEP experiments, PoS ICHEP2022, 1237 (2022) [Google Scholar]
  31. J.M. Campbell, M. Diefenthaler, T.J. Hobbs, S. Höche, J. Isaacson, F. Kling, S. Mrenna, J. Reuter, S. Alioli, J.R. Andersen et al., Event generators for high-energy physics experiments, SciPost physics 16, 130 (2024) [Google Scholar]
  32. J. Albrecht et al. (HEP Software Foundation), A Roadmap for HEP Software and Computing R&D for the 2020s, Comput. Softw. Big Sci. 3, 7 (2019), 1712.06982 [Google Scholar]
  33. S. Amoroso et al. (HSF Physics Event Generator WG), Challenges in Monte Carlo Event Generator Software for High-Luminosity LHC, Comput. Softw. Big Sci. 5, 12 (2021), 2004.13687 [CrossRef] [Google Scholar]
  34. E. Yazgan et al. (HSF Physics Event Generator WG), HL-LHC Computing Review Stage-2, Common Software Projects: Event Generators (2021), 2109.14938 [Google Scholar]
  35. U. Hernandez Acosta, A. Reinhard, S. Ehrig, T. Jungnickel, K. Steiniger, QuantumElectrodynamics.jl, https://github.com/QEDjl-project [Google Scholar]
  36. A. Fedotov, A. Ilderton, F. Karbstein, B. King, D. Seipt, H. Taya, G. Torgrimsson, Advances in QED with intense background fields, Phys. Rept. 1010, 1 (2023), 2203.00019 [Google Scholar]
  37. S. Agostinelli et al. (GEANT4), GEANT4 - A Simulation Toolkit, Nucl. Instrum. Meth. A 506, 250 (2003) [CrossRef] [Google Scholar]
  38. J. Allison and et al., Recent developments in Geant4, Nucl. Instruments and Methods in Phys. Research Section A 835, 186 (2016), https://doi.org/10.1016/j.nima.2016.06.125 [Google Scholar]
  39. M. Ali, M.A. Charaf, M.K. Charaf, A. Bocci, P. Gras, R.E. Houssami, Porting the CMS pixel reconstruction to Julia: preliminary results (2024), https://indico.cern.ch/event/1410341/contributions/6135572/ [Google Scholar]
  40. M. Cacciari, G.P. Salam, G. Soyez, FastJet User Manual, Eur. Phys. J. C 72, 1896 (2012), 1111.6097 [CrossRef] [Google Scholar]
  41. G.A. Stewart, P. Gras, B. Hegner, A. Krasnopolski, Polyglot Jet Finding, EPJ Web of Conf. 295, 05017 (2024), https://doi.org/10.1051/epjconf/202429505017 [Google Scholar]
  42. G.A. Stewart, S. Ganguly, A. Krasnopolski, P. Gras, S. Ghosh, Fast Jet Finding in Julia, EPJ Web of Conf. (CHEP2024) (in press) [Google Scholar]
  43. S. Danisch, J. Krumbiegel, Makie.jl: Flexible high-performance data visualization for Julia, Journal of Open Source Software 6, 3349 (2021), https://doi.org/10.21105/joss.03349 [Google Scholar]
  44. M. Stanitzki, J. Strube, Performance of Julia for High Energy Physics Analyses, Comput. Softw. Big Sci. 5, 10 (2021), 2003.11952, https://doi.org/10.1007/s41781-021-00053-3 [CrossRef] [Google Scholar]
  45. R. Aaij et al., Study of the doubly charmed tetraquark T+, Nature Communications 13, 3351 (2022), https://doi.org/10.1038/s41467-022-30206-w [Google Scholar]
  46. R. Aaij et al. (LHCb Collaboration), Observation of excited Ω0c baryons in Ω−b Ξ+cK−π− decays, Phys. Rev. D 104, L091102 (2021), https://link.aps.org/doi/10.1103/PhysRevD.104.L091102 [Google Scholar]
  47. Ł. Bibrzycki, C. Fernández-Ramírez, V. Mathieu, M. Mikhasenko, M. Albaladejo, A.N. Hiller Blin, A. Pilloni, A.P. Szczepaniak, π− p →η′π− p in the double-Regge region, The European Physical Journal C 81, 647 (2021), https://doi.org/10.1140/epjc/s10052-021-09420-1 [Google Scholar]
  48. N. Abgrall at al., The large enriched germanium experiment for neutrinoless double beta decay (LEGEND) (AIP, 2017), Vol. 1894 of American Institute of Physics Conference Series, p. 020027, 1709.01980 [Google Scholar]
  49. O. Schulz, F. Beaujean, A. Caldwell, C. Grunwald, V. Hafych, K. Kröninger, S.L. Cagnina, L. Röhrig, L. Shtembari, BAT.jl: A Julia-Based Tool for Bayesian Inference, SN Computer Science 2, 210 (2021), https://doi.org/10.1007/s42979-021-00626-4 [CrossRef] [Google Scholar]
  50. I. Abt, F. Fischer, F. Hagemann, L. Hauertmann, O. Schulz, M. Schuster, A.J. Zsigmond, Simulation of semiconductor detectors in 3D with SolidStateDetectors.jl, Journal of Instrumentation 16, P08007 (2021), https://doi.org/10.1088/1748-0221/16/08/p08007 [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.