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Specific informations of {{{1}}}'s site:
- [[{{{1}}}:Hardware|Hardware]]
- [[{{{1}}}:Network|Network]]
- [[{{{1}}}:Storage|Storage]]
- [[{{{1}}}:External access|External access]]
- [[{{{1}}}:People|People]]
- [[{{{1}}}:Roadmap|Roadmap]]
- [[{{{1}}}:Logs|Logs]]
- [[{{{1}}}:Administration|Administration]]
Direct access to resources for {{{1}}}'s site:
- reservation state and reservation history via OAR
- available resources via Ganglia
- critical services via Nagios
- opened bugs via BugZilla
- {{{1}}}-staff via mail
Shortcuts to global tools and informations:
- support procedures to report bugs or ask for enhancements
- global reservation state and global reservation history via OARgrid.
- registered users via phpLDAPadmin
Latest updated publications Five random publications that benefited from Grid'5000 (at least 2932 overall):
- Reda Khoufache, Anisse Belhadj, Hanene Azzag, Mustapha Lebbah. Distributed MCMC inference for Bayesian Non-Parametric Latent Block Model. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), May 2024, Taipei, Taiwan. hal-04457575 view on HAL pdf
- Rémi Meunier, Thomas Carle, Thierry Monteil. Multi-core interference over-estimation reduction by static scheduling of multi-phase tasks. Real-Time Systems, 2024, pp.1--39. 10.1007/s11241-024-09427-3. hal-04689317 view on HAL pdf
- Albert d'Aviau de Piolant, Hayfa Tayeb, Bérenger Bramas, Mathieu Faverge, Abdou Guermouche, et al.. Improving energy efficiency of HPC applications using unbalanced GPU power capping. HCW (Ipdps workshop), Jun 2025, Milan (Italie), Italy. hal-04883872v2 view on HAL pdf
- Baptiste Jonglez, Matthieu Simonin, Jolan Philippe, Sidi Mohammed Kaddour. Multi-provider capabilities in EnOSlib: driving distributed system experiments on the edge-to-cloud continuum. DAIS 2025: 25th International Conference on Distributed Applications and Interoperable Systems, Jun 2025, Lille, France. pp.25-42, 10.1007/978-3-031-95728-4_2. hal-05052776 view on HAL pdf
- Rahma Hellali, Zaineb Chelly Dagdia, Karine Zeitouni. A Multi-Objective Multi-Agent Interactive Deep Reinforcement Learning Approach for Feature Selection. International conference on neural information processing, Dec 2024, Auckland (Nouvelle Zelande), New Zealand. pp.15. hal-04723314 view on HAL pdf
Latest updated experiments {{#experiments: 5|{{{1}}}}}
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