Open Access
Issue
EPJ Web Conf.
Volume 251, 2021
25th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2021)
Article Number 02045
Number of page(s) 11
Section Distributed Computing, Data Management and Facilities
DOI https://doi.org/10.1051/epjconf/202125102045
Published online 23 August 2021
  1. A. Rizzi, G. Petrucciani, M. Peruzzi, A further reduction in CMS event data for analysis: the NANOAOD format, EPJ Web Conf., 214 (2019), doi: 10.1051/epjconf/201921406021 [Google Scholar]
  2. S. Chatrchyan et al. “The CMS Experiment at the CERN LHC”. In: JINST 3 (2008), S08004. DOI: 10.1088/1748-0221/3/08/S08004 [Google Scholar]
  3. CMS Offline Software and Computing, Evolution of the CMS Computing Model towards Phase-2, CMS-NOTE-2021-001, https://cds.cern.ch/record/2751565 [Google Scholar]
  4. E. Bocchi, L. Canali, D. Castro, P. Kothuri, H. G. Labrador, ScienceBox Converging to Kubernetes containers in production for on-premise and hybrid clouds for CERNBox, SWAN, and EOS, EPJ Web Conf. 245 (2020) 07047, doi: 10.1051/epjconf/202024507047 [Google Scholar]
  5. 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]
  6. D. Spiga, and others, Exploiting private and commercial clouds to generate on-demand CMS computing facilities with DODAS, EPJ Web Conf. 214 (2019), doi: 10.1051/epjconf/201921407027 [Google Scholar]
  7. https://jupyter.org/hub [Google Scholar]
  8. https://research.cs.wisc.edu/htcondor/ [Google Scholar]
  9. https://spark.apache.org/ [Google Scholar]
  10. https://kubernetes.io/ [Google Scholar]
  11. https://helm.sh/ [Google Scholar]
  12. https://github.com/roboll/helmfile [Google Scholar]
  13. https://indigo-iam.github.io/docs/v/current/about.html [Google Scholar]
  14. https://kubernetes.github.io/ingress-nginx/ [Google Scholar]
  15. https://cert-manager.io/ [Google Scholar]
  16. https://longhorn.io/ [Google Scholar]
  17. https://min.io [Google Scholar]
  18. Buncic, P. and Aguado Sanchez, C. and Blomer, J. and Franco, L. and Harutyunian, A. and Mato, P. and Yao, Y., CernVM: A virtual software appliance for LHC applications, J. Phys. Conf. Ser., 219 (2010), doi: 10.1088/1742-6596/219/4/042003 [Google Scholar]
  19. K. Bloom and others, Any Data, Any Time, Anywhere: Global Data Access for Science, arXiv physics.comp-ph , 1508.01443, 8 (2015) [Google Scholar]
  20. https://jupyterhub-kubespawner.readthedocs.io/en/latest/index.html [Google Scholar]
  21. https://indigo-dc.gitbook.io/oidc-agent/ [Google Scholar]
  22. https://projectescape.eu/ [Google Scholar]
  23. D. Ciangottini, G. Bagliesi, M. Biasotto, T. Boccali, D. Cesini, G. Donvito, A. Falabella, E. Mazzoni, D. Spiga, M. Tracolli, Integration of the Italian cache federation within the CMS computing model, PoS ISGC2019, p. 014 (2019), doi: 10.22323/1.351.0014 [Google Scholar]
  24. https://xrootd.slac.stanford.edu [Google Scholar]
  25. https://github.com/kubernetes-sigs/kube-batch [Google Scholar]
  26. V. E. Padulano, J. C. Villanueva, E. Guiraud, and E. T. Saavedra, Distributed data analysis with ROOT RDataFrame, EPJ Web Conf. Volume 245, 2020, doi: https://doi.org/10.1051/epjconf/202024503009 [Google Scholar]
  27. https://prometheus.io/ [Google Scholar]
  28. https://kubernetes.io/docs/tasks/run-application/horizontal-pod-autoscale/ [Google Scholar]
  29. https://docs.oasis-open.org/tosca/TOSCA-Simple-Profile-YAML/v1.0/csprd01/TOSCA-Simple-Profile-YAML-v1.0-csprd01.html#_Toc430015628 [Google Scholar]
  30. https://www.ansible.com/ [Google Scholar]
  31. Green, D. R., Meade, P., & Pleier, M. A. (2017). Multiboson interactions at the LHC. Reviews of Modern Physics, 89(3), 035008 [Google Scholar]
  32. Spiga, D., Lacaprara, S., Bacchi, W., Cinquilli, M., Codispoti, G., Corvo, M., ... & Kavka, C. (2007, December). The CMS remote analysis builder (CRAB). In International Conference on High-Performance Computing (pp. 580-586). Springer, Berlin, Heidelberg [Google Scholar]
  33. https://root.cern/manual/python/ [Google Scholar]
  34. https://github.com/cms-nanoAOD/nanoAOD-tools [Google Scholar]
  35. https://uproot.readthedocs.io/ [Google Scholar]
  36. https://scikit-learn.org/stable/ [Google Scholar]
  37. https://www.tensorflow.org/ [Google Scholar]
  38. https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html [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.