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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}}}: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 3009 overall):
- Maurice Brémond, Hugo Brunie, Laurent Debreu, Rupert W Ford, Florian Lemarié, et al.. Poseidon: A Source-to-Source Translator for Holistic HPC Optimizations of Ocean Models on Regular Grids. SC 2024 - International Conference for High Performance Computing, Networking, Storage, and Analysis, Nov 2024, Atlanta (Georgia), United States. , pp.1-1, 2024, 10.5281/zenodo.11190458. hal-04811677 view on HAL pdf
- Georges da Costa. Hardware and application aware performance, power and energy models for modern HPC servers with DVFS. Sustainable Computing : Informatics and Systems, 2025, 46, pp.101106. 10.1016/j.suscom.2025.101106. hal-04983485 view on HAL pdf
- Louis Roussel. Integral equations modelling and deep learning. Machine Learning cs.LG. Université de Lille, 2025. English. NNT : 2025ULILB033. tel-05567799v2 view on HAL pdf
- Pierre Jacquet, Maxime Agusti, Eddy Caron, Camille Coti, Marcos Dias de Assunção, et al.. Untangling GPU Power Consumption: Job-Level Inference in Cloud Shared Settings. EUROSYS 2026 - European Conference on Computer Systems, ACM, Apr 2026, Edinbourg, Ecosse, United Kingdom. pp.624-640, 10.1145/3767295.3769333. hal-05291033 view on HAL pdf
- Sofya Dymchenko, Abhishek Purandare, Bruno Raffin. MelissaDL x Breed: Towards Data-Efficient On-line Supervised Training of Multi-parametric Surrogates with Active Learning. AI4S 2024 - 5th Workshop on artificial intelligence and machine learning for scientific applications, Nov 2024, Atlanta (Georgia), United States. pp.1-9. hal-04712480 view on HAL pdf
Latest updated experiments {{#experiments: 5|{{{1}}}}}
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