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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]]
- Administration
Direct access to resources for {{{1}}}'s site:
- reservation state (monika) and reservation history (drawgantt) via OAR
- available resources via Ganglia
- critical services via Nagios
- opened bugs via BugZilla
- support-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 UMS
Latest updated publications Five random publications that benefited from Grid'5000 (at least 2927 overall):
- Dorian Goepp, Samuel Brun, Quentin Guilloteau, Olivier Richard. Un prototype de cache de métadonnées pour le passage à l'échelle de NixOS-Compose. COMPAS 2024 - Conférence francophone d'informatique en Parallélisme, Architecture et Système, Jul 2024, Nantes, France. pp.1-8. hal-04632952 view on HAL pdf
- Wedan Emmanuel Gnibga, Anne Blavette, Anne-Cécile Orgerie. Renewable Energy in Data Centers: the Dilemma of Electrical Grid Dependency and Autonomy Costs. IEEE Transactions on Sustainable Computing, 2024, 9 (3), pp.315-328. 10.1109/TSUSC.2023.3307790. hal-04189173 view on HAL pdf
- Mateusz Gienieczko, Filip Murlak, Charles Paperman. Supporting Descendants in SIMD-Accelerated JSONPath. International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS 2024), 2024, San Diego (California), United States. pp.338-361, 10.4230/LIPIcs. hal-04398350 view on HAL pdf
- Pierre-François Gimenez, Jérôme Mengin. Learning Conditional Preference Networks: an Approach Based on the Minimum Description Length Principle. IJCAI 2024 - 33rd International Joint Conference on Artificial Intelligence, Aug 2024, Jeju, South Korea. pp.3395-3403, 10.24963/ijcai.2024/376. hal-04572196 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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