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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:
- Template:Url/monika.cgi 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 3010 overall):
- Jan Aalmoes. Intelligence artificielle pour des services moraux : Concilier équité et confidentialité. Intelligence artificielle cs.AI. INSA de Lyon, 2024. Français. NNT : 2024ISAL0126. tel-05014177 view on HAL pdf
- Shadi Ibrahim, Jad Darrous. Erasure Coding Aware Block Placement for Data-Intensive Applications. CHEOPS 2025 - 5th Workshop on Challenges and Opportunities of Efficient and Performant Storage Systems, Mar 2025, Rotterdam Netherlands, Netherlands. pp.15-22, 10.1145/3719330.3721229. hal-05016741 view on HAL pdf
- Cédric Prigent, Melvin Chelli, Alexandru Costan, Loïc Cudennec, René Schubotz, et al.. Efficient Resource-Constrained Federated Learning Clustering with Local Data Compression on the Edge-to-Cloud Continuum. HiPC 2024 - 31st IEEE International Conference on High Performance Computing, Data, and Analytics, Dec 2024, Bengaluru (Bangalore), India. pp.1-11, 10.1109/HiPC62374.2024.00033. hal-04779813 view on HAL pdf
- Thomas Bouvier. Distributed Rehearsal Buffers for Continual Learning at Scale. Machine Learning cs.LG. INSA de Rennes, 2024. English. NNT : 2024ISAR0011. tel-04986111 view on HAL pdf
- Mai Huong Do, Millian Poquet, Georges da Costa. FedE-ator : A framework for energy consumption analysis of federated learning in distributed systems. Compas’2025 : Parallélisme / Architecture/ Système, Jul 2025, Bordeaux, France. hal-05181877 view on HAL pdf
Latest updated experiments {{#experiments: 5|{{{1}}}}}
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