EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 251 (2021) EPJ Web Conf., 251 (2021) 02061 References
Open Access
Issue
EPJ Web Conf.
Volume 251, 2021
25th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021)
Article Number 02061
Number of page(s) 9
Section Distributed Computing, Data Management and Facilities
DOI https://doi.org/10.1051/epjconf/202125102061
Published online 23 August 2021
  1. Bloch, I. The LHC Physics Center. Nuclear Physics B-Proceedings Supplements, 177, pp.261–262, Elsevier, 2008. [Google Scholar]
  2. Adolphi, Roman, et al. The CMS experiment at the CERN LHC. Jinst, 803: S08004, 2008. [Google Scholar]
  3. Petrucciani, Giovanni, et al. Mini-AOD: A new analysis data format for CMS. Journal of Physics: Conference Series. Vol. 664. No. 7. IOP Publishing, 2015. [Google Scholar]
  4. Rizzi, Andrea, Giovanni Petrucciani, and Marco Peruzzi. A further reduction in CMS event data for analysis: the NANOAOD format., EPJ Web of Conferences. Vol. 214., EDP Sciences, 2019. [Google Scholar]
  5. Travis E. Oliphant. Python for Scientific Computing, Computing in Science & Engineering, 9, 10–20 (2007), DOI: 10.1109/MCSE.2007.58 [Google Scholar]
  6. Thomas Kluyver, et al., Jupyter Notebooks - a publishing format for reproducible computational workflows, DOI: 10.3233/978-1-61499-649-1-87 [Google Scholar]
  7. J. Pivarski, P. Elmer, D. Lange, Awkward Arrays in Python, C++, and Numba, arXiv arXiv:2001.06307 [Google Scholar]
  8. Danilo Piparo, Philippe Canal, Enrico Guiraud, Xavier Valls Pla, Gerardo Ganis, Guilherme Amadio, Axel Naumann and Enric Tejedor RDataFrame: Easy Parallel ROOT Analysis at 100 Threads EPJ Web Conf., 214 (2019) 06029 DOI: https://doi.org/10.1051/epjconf/201921406029 [Google Scholar]
  9. Shadura, Oksana. (2020, July). A prototype U.S. CMS analysis facility. Presented at the PyHEP 2020 Workshop, Zenodo. http://doi.org/10.5281/zenodo.4136273 [Google Scholar]
  10. Nicholas Smith, Lindsey Gray, Matteo Cremonesi, Bo Jayatilaka, Oliver Gutsche, Allison Hall, Kevin Pedro, Maria Acosta, Andrew Melo, Stefano Belforte and Jim Pivarski, Coffea Columnar Object Framework For Effective Analysis, EPJ Web Conf., 245, 06012 (2020) [Google Scholar]
  11. K. Bloom, US CMS Tier-2 computing 2008 J. Phys.: Conf. Ser. 119 052004 [Google Scholar]
  12. Douglas Thain, Todd Tannenbaum, and Miron Livny, “Distributed Computing in Practice: The Condor Experience”, Concurrency and Computation: Practice and Experience, Vol. 17, No. 2-4, pages 323–356, February-April, 2005. [Google Scholar]
  13. Kelsey Hightower, Brendan Burns, and Joe Beda. Kubernetes: Up and Running Dive into the Future of Infrastructure (1st. ed.). O’Reilly Media, Inc. 2017 [Google Scholar]
  14. www.jupyter.org. (n.d.). Project Jupyter. [online] Available at: https://jupyter.org/hub. [Google Scholar]
  15. Atlassian (n.d.). Is GitOps the next big thing in DevOps? | Atlassian Git Tutorial. [online] Atlassian. Available at: https://www.atlassian.com/git/tutorials/gitops [Accessed 28 Feb. 2021]. [Google Scholar]
  16. GitHub. (2021). fluxcd/flux. [online] Available at: https://github.com/fluxcd/flux [Accessed 28 Feb. 2021]. [Google Scholar]
  17. Helm.sh. (2019). Helm. [online] Available at: https://helm.sh/ [Accessed 28 Oct. 2019]. [Google Scholar]
  18. Rocklin, Matthew. Dask: Parallel computation with blocked algorithms and task scheduling., Proceedings of the 14th python in science conference. Vol. 126., Austin, TX: SciPy, 2015. [Google Scholar]
  19. Van Der Walt Stefan, S. Chris Colbert, and Gael Varoquaux. The NumPy array: a structure for efficient numerical computation. Computing in science & engineering 13.2 (2011): 22–30. [Google Scholar]
  20. McKinney, Wes. pandas: a foundational Python library for data analysis and statistics. Python for High Performance and Scientific Computing 14.9 (2011): 1–9. [Google Scholar]
  21. Pedregosa, Fabian, et al. Scikit-learn: Machine learning in Python. The Journal of machine Learning research 12 (2011): 2825–2830. [Google Scholar]
  22. Eastlake, Donald. Transport layer security (TLS) extensions: Extension definitions. RFC 6066, January, 2011. [Google Scholar]
  23. Traefik Labs: Makes Networking Boring. (n.d.). Traefik Labs: Makes Networking Boring. [online] Available at: https://traefik.io/., accessed: 2021-02-25 [Google Scholar]
  24. GitHub. (2021). dask/dask-jobqueue. [online] Available at: https://github.com/dask/dask-jobqueue [Accessed 28 Feb. 2021]. [Google Scholar]
  25. GitHub. (2021). dask/dask-labextension. [online] Available at: https://github.com/dask/dask-labextension [Accessed 28 Feb. 2021]. [Google Scholar]
  26. hub.docker.com. (n.d.). Docker Hub. [online] Available at: https://hub.docker.com/u/coffeateam [Accessed 28 Feb. 2021]. [Google Scholar]
  27. GitHub. (2021). CoffeaTeam/coffea-casa. [online] Available at: https://github.com/CoffeaTeam/coffea-casa [Accessed 28 Feb. 2021]. [Google Scholar]
  28. Sakimura, Natsuhiko, et al. Openid connect core 1.0., The OpenID Foundation (2014): S3. [Google Scholar]
  29. cms-auth.web.cern.ch. (n.d.). INDIGO IAM for cms-Log in. [online] Available at: https://cms-auth.web.cern.ch [Accessed 28 Feb. 2021]. [Google Scholar]
  30. Von Welch, et al. X. 509 proxy certificates for dynamic delegation. 3rd annual PKI R&D workshop. Vol. 14. 2004. [Google Scholar]
  31. Fajardo, Edgar, et al. Creating a content delivery network for general science on the internet backbone using XCaches., EPJ Web of Conferences. Vol. 245., EDP Sciences, 2020. [Google Scholar]
  32. K. Bloom, et al., Any data, any time, anywhere: Global data access for science, in 2015 IEEE/ACM 2nd International Symposium on Big Data Computing (BDC), pp. 85–91. 2015. DOI: 10.1109/BDC.2015.33. [Google Scholar]
  33. Bockelman, B.P. (2020). bbockelm/xrdcl-authz-plugin. [online] GitHub. Available at: https://github.com/bbockelm/xrdcl-authz-plugin [Accessed 28 Feb. 2021]. [Google Scholar]
  34. CMS Collaboration, Search for the production of four top quarks at the CMS experiment at ps= 13 TeV, JHEP 11 (2019) 082. [Google Scholar]
  35. CMS Collaboration, Search for associated production of a Higgs boson and a single top quark in proton-proton collisions at ps= 13 TeV. Physical Review D, 99(9), 092005 (2019). [Google Scholar]
  36. GitHub. (2021). Jupyterhub/Binderhub. [online] Available at: https://github.com/jupyterhub/binderhub [Accessed 28 Feb. 2021]. [Google Scholar]
  37. Galewsky, B., et al. ServiceX A Distributed, Caching, Columnar Data Delivery Service., EPJ Web of Conferences. Vol. 245., EDP Sciences, 2020. [Google Scholar]
  38. Jeff LeFevre, Carlos Maltzahn. SkyhookDM: Data Processing in Ceph with Programmable Storage., retrieved from https://par.nsf.gov/biblio/10182302. USENIX login; 45.2, accessed: 2021-02-25 [Google Scholar]
  39. Rene Brun and Fons Rademakers, ROOT - An Object Oriented Data Analysis Framework, Proceedings AIHENP 96 Workshop, Lausanne, Sep. 1996, Nucl. Inst. & Meth. in Phys. Res. A 389 (1997) 81–86. [Google Scholar]
  40. Weil, Sage A., et al. Ceph: A scalable, high-performance distributed file system. Proceedings of the 7th symposium on Operating systems design and implementation. 2006. [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.