Grid5000:Home: Difference between revisions
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Key features: | Key features: | ||
* provides '''access to a large amount of resources''': 12000 cores, 800 compute-nodes grouped in homogeneous clusters, and featuring various technologies: GPU, SSD, NVMe, 10G Ethernet, Infiniband, | * provides '''access to a large amount of resources''': 12000 cores, 800 compute-nodes grouped in homogeneous clusters, and featuring various technologies: GPU, SSD, NVMe, 10G and 25G Ethernet, Infiniband, Omni-Path | ||
* '''highly reconfigurable and controllable''': researchers can experiment with a fully customized software stack thanks to bare-metal deployment features, and can isolate their experiment at the networking layer | * '''highly reconfigurable and controllable''': researchers can experiment with a fully customized software stack thanks to bare-metal deployment features, and can isolate their experiment at the networking layer | ||
* '''advanced monitoring and measurement features for traces collection of networking and power consumption''', providing a deep understanding of experiments | * '''advanced monitoring and measurement features for traces collection of networking and power consumption''', providing a deep understanding of experiments | ||
Revision as of 09:05, 9 November 2018
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Grid'5000 is a large-scale and versatile testbed for experiment-driven research in all areas of computer science, with a focus on parallel and distributed computing including Cloud, HPC and Big Data. Key features:
Older documents:
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Random pick of publications
Five random publications that benefited from Grid'5000 (at least 2932 overall):
- Cassandre Vey, Adrien van den Bossche, Réjane Dalcé, Georges da Costa, Olivier Negro, et al.. Experimenting IoT-Edge-Cloud- HPC Continuum on Existing Platforms. 2025 IEEE 25th International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW), IEEE, May 2025, Tromsø Norway, Norway. 10.1109/CCGridW65158.2025.00026. hal-05147272 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
- Houssem Ouertatani. Efficient Deep Neural Architecture Search via Bayesian Optimization : An application to Computer Vision. Computer Vision and Pattern Recognition cs.CV. Université de Lille, 2024. English. NNT : 2024ULILB044. tel-05014154 view on HAL pdf
- Gaël Vila, Emmanuel Medernach, Inés Gonzalez, Axel Bonnet, Yohan Chatelain, et al.. The Impact of Hardware Variability on Applications Packaged with Docker and Guix: a Case Study in Neuroimaging. ACM REP'24, ACM, Jun 2024, Rennes, France. pp.75-84, 10.1145/3641525.3663626. hal-04480308v2 view on HAL pdf
- Alan Lira Nunes, Cristina Boeres, Lúcia Maria de A. Drummond, Laércio Lima Pilla. Optimal Time and Energy-Aware Client Selection Algorithms for Federated Learning on Heterogeneous Resources. 2024 IEEE 36th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), Nov 2024, Hilo, France. pp.148-158, 10.1109/SBAC-PAD63648.2024.00021. hal-04690494v2 view on HAL pdf
Latest news
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Grid'5000 sites
Current funding
As from June 2008, Inria is the main contributor to Grid'5000 funding.
INRIA |
CNRS |
UniversitiesUniversité Grenoble Alpes, Grenoble INP |
Regional councilsAquitaine |