EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 245 (2020) EPJ Web Conf., 245 (2020) 03005 References
Open Access
Issue
EPJ Web Conf.
Volume 245, 2020
24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019)
Article Number 03005
Number of page(s) 15
Section 3 - Middleware and Distributed Computing
DOI https://doi.org/10.1051/epjconf/202024503005
Published online 16 November 2020
  1. R. Pordes, D. Petravick, B. Kramer, D. Olson, M. Livny, A. Roy, P. Avery, K. Blackburn, T. Wenaus, F. Würthwein et al., The open science grid, in J. Phys. Conf. Ser. (2007), Vol. 78 of 78, p. 012057 [Google Scholar]
  2. I. Sfiligoi, D.C. Bradley, B. Holzman, P. Mhashilkar, S. Padhi, F. Wurthwein, The pilot way to grid resources using glideinWMS, in 2009 WRI World Congress on Computer Science and Information Engineering (2009), Vol. 2 of 2, pp. 428–432 [CrossRef] [Google Scholar]
  3. P. Buncic, C.A. Sanchez, J. Blomer, L. Franco, A. Harutyunian, P. Mato, Y. Yao, CernVM a virtual software appliance for LHC applications, in J. Phys. Conf. Ser. (2010), Vol. 219, p. 042003 [CrossRef] [Google Scholar]
  4. I. Bird, Computing for the Large Hadron Collider, in Annual Review of Nuclear and Particle Science (2011), Vol. 61, pp. 99–118 [CrossRef] [Google Scholar]
  5. D. Weitzel, B. Bockelman, D.A. Brown, P. Couvares, F. Würthwein, E.F. Hernandez, Data Access for LIGO on the OSG, in Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact (2017), PEARC 17, pp. 1–6 [Google Scholar]
  6. L. Bauerdick, K. Bloom, B. Bockelman, D. Bradley, S. Dasu, J. Dost, I. Sfiligoi, A. Tadel, M. Tadel, F. Wuerthwein et al., XRootd, disk-based, caching proxy for optimization of data access, data placement and data replication, in J. Phys. Conf. Ser. (2014), Vol. 513 [Google Scholar]
  7. D. Weitzel, M. Zvada, I. Vukotic, R. Gardner, B. Bockelman, M. Rynge, E. Hernandez, B. Lin, M. Selmeci, StashCache: A Distributed Caching Federation for the Open Science Grid, in Proceedings of the Practice and Experience in Advanced Research Computing (2019), PEARC 19, pp. 1–7 [Google Scholar]
  8. K. Bloom, the CMS Collaboration, CMS Use of a Data Federation, in J. Phys. Conf. Ser. (2014), Vol. 513, p. 042005 [Google Scholar]
  9. M. Barisits, T. Beermann, F. Berghaus, B. Bockelman, J. Bogado, D. Cameron, D. Christidis, D. Ciangottini, G. Dimitrov, M. Elsing et al., Rucio: Scientific Data Management, in Computing and Software for Big Science (2019), Vol. 3, p. 11 [Google Scholar]
  10. XENON Collaboration, Software for the XENONnT experiment (2019), https://github.com/XENONnT [Google Scholar]
  11. XENON Collaboration, Streaming analysis for xenon experiments (2019), https://github.com/AxFoundation/strax [Google Scholar]
  12. E. Deelman, K. Vahi, G. Juve, M. Rynge, S. Callaghan, P.J. Maechling, R. Mayani, W. Chen, R. Ferreira da Silva, M. Livny et al., Pegasus: a Workflow Management System for Science Automation, in Future Generation Computer Systems (2015), Vol. 46, pp. 17–35 [CrossRef] [Google Scholar]
  13. G.M. Kurtzer, V. Sochat, M.W. Bauer, Singularity: Scientific containers for mobility of compute, in PLOS ONE (Public Library of Science, 2017), Vol. 12, pp. 1–20 [Google Scholar]
  14. CernVM-FS Documentation (2019), https://cvmfs.readthedocs.io [Google Scholar]
  15. RADOS GATEWAY, https://docs.ceph.com/docs/bobtail/radosgw [Google Scholar]
  16. D. Heck, J. Knapp, J. Capdevielle, G. Schatz, T. Thouw, CORSIKA: A Monte Carlo code to simulate extensive air showers, in Forschungszentrum Karlsruhe Report FZKA 6019 (1998) [Google Scholar]
  17. R. Sunyaev, Y. Zel’dovich, The Interaction of Matter and Radiation in a Hot-Model Universe, Astrophysics and Space Science (1969), Vol. 4, pp. 301–316 [Google Scholar]
  18. T.B. Littenberg, N.J. Cornish, Bayesian inference for spectral estimation of gravitational wave detector noise, in Phys. Rev. D (2015), Vol. 91, p. 084034 [CrossRef] [Google Scholar]
  19. C.M. Biwer, C.D. Capano, S. De, M. Cabero, D.A. Brown, A.H. Nitz, V. Raymond, PyCBC Inference: A Python-based Parameter Estimation Toolkit for Compact Binary Coalescence Signals, in Publications of the Astronomical Society of the Pacific (IOP Publishing, 2019), Vol. 131, p. 024503 [Google Scholar]
  20. LIGO Scientific Collaboration, LIGO Algorithm Library LALSuite, free software (GPL) (2018), https://git.ligo.org/lscsoft/lalsuite [Google Scholar]
  21. J. Lange, R. O’Shaughnessy, M. Rizzo, Rapid and accurate parameter inference for coalescing, precessing compact binaries, in arXiv:1805.10457 [gr-qc] (2018) [Google Scholar]
  22. D. Wysocki, R. O’Shaughnessy, J. Lange, Y.L.L. Fang, Accelerating parameter inference with graphics processing units, in Phys. Rev. D (American Physical Society, 2019), Vol. 99, p. 084026 [Google Scholar]
  23. Elastic Search, https://www.elastic.co [Google Scholar]
  24. L. Bryant, J. Van, B. Riedel, R. Gardner, J.C. Bejar, J. Hover, B. Tovar, K. Hurtado, D. Thain, VC3: A Virtual Cluster Service for Community Computation, in Proceedings of Practice and Experience in Advanced Research Computing (2018), PEARC 18, pp. 1–8 [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.