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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]]
- internal 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 2931 overall):
- Reda Khoufache, Anisse Belhadj, Mustapha Lebbah, Hanene Azzag. Distributed MCMC Inference for Bayesian Non-parametric Latent Block Model. 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, May 2024, Taipei, Taiwan. pp.271-283, 10.1007/978-981-97-2242-6_22. hal-04623748 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
- Guillaume Helbecque, Ezhilmathi Krishnasamy, Nouredine Melab, Pascal Bouvry. GPU-Accelerated Tree-Search in Chapel versus CUDA and HIP. 14th IEEE Workshop Parallel / Distributed Combinatorics and Optimization (PDCO 2024), May 2024, San Francisco, United States. 10.1109/IPDPSW63119.2024.00156. hal-04551856 view on HAL pdf
- Vladimir Ostapenco, Laurent Lefèvre, Anne-Cécile Orgerie, Benjamin Fichel. Exploring RAPL as a Power Capping Leverage for Power-Constrained Infrastructures. ICA3PP 2024 - 24th International Conference on Algorithms and Architectures for Parallel Processing, Oct 2024, Macau SAR, China. pp.1-10. hal-04742418 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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