Grid5000:Home: Difference between revisions
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Key features: | Key features: | ||
* provides '''access to a large amount of resources''': 15000 cores, 800 compute-nodes grouped in homogeneous clusters, and featuring various technologies: GPU, SSD, NVMe, 10G and 25G Ethernet, Infiniband, Omni-Path | * provides '''access to a large amount of resources''': 15000 cores, 800 compute-nodes grouped in homogeneous clusters, and featuring various technologies: PMEM, 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 00:57, 12 February 2020
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Grid'5000 is a large-scale and flexible 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 and AI. Key features:
Grid'5000 is merging with FIT to build the SILECS Infrastructure for Large-scale Experimental Computer Science. Read an Introduction to SILECS (April 2018)
Older documents:
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Random pick of publications
Five random publications that benefited from Grid'5000 (at least 2998 overall):
- Marc Jourdan, Clémence Réda. An Anytime Algorithm for Good Arm Identification. 2024. hal-04688141 view on HAL pdf
- Cédric Prigent. Towards Efficient and Trustworthy Federated Learning on the Computing Continuum. Machine Learning cs.LG. INSA de Rennes, 2025. English. NNT : 2025ISAR0003. tel-05279213 view on HAL pdf
- Anna Nikiforovskaya, Claire Gardent. Evaluating Knowledge Graph-to-text Generation Models for English and Russian on Out Of Domain Data. 17th International Natural Language Generation Conference, Sep 2024, Tokyo, Japan. hal-04854968 view on HAL pdf
- Cherif Latreche, Nikos Parlavantzas, Hector A Duran-Limon. FoRLess: A Deep Reinforcement Learning-based approach for FaaS Placement in Fog. UCC 2024 - 17th IEEE/ACM International Conference on Utility and Cloud Computing, Dec 2024, Sharjah, United Arab Emirates. pp.1-9. hal-04791252 view on HAL pdf
- Ndeye-Emilie Mbengue. In Data or Invisible: Toward a Better Digital Representation of Low-Resource Languages with Knowledge Graphs. ESWC 2026 - 23rd European Semantic Web Conference, May 2026, Dubrovnik, Croatia. hal-05608081 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 |