Grid5000:Home: Difference between revisions
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* 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 23:57, 11 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 2937 overall):
- Sorina Camarasu-Pop. Computational Reproducibility. 3rd cycle. 12th SLEIGHT Science Event, Saint Etienne (FR), France. 2024. hal-04649287 view on HAL pdf
- Pierre-François Gimenez, Jérôme Mengin. Learning Conditional Preference Networks: an Approach Based on the Minimum Description Length Principle. IJCAI 2024 - 33rd International Joint Conference on Artificial Intelligence, Aug 2024, Jeju, South Korea. pp.3395-3403, 10.24963/ijcai.2024/376. hal-04572196 view on HAL pdf
- Louis Roussel. Integral Equations Modelling and Deep Learning. Computer Science cs. Université de Lille, 2025. English. NNT : . tel-05425240 view on HAL pdf
- Vincent Alba, Olivier Aumage, Denis Barthou, Raphaël Colin, Marie-Christine Counilh, et al.. Performance portability of generated cardiac simulation kernels through automatic dimensioning and load balancing on heterogeneous nodes. PDSEC 2024, May 2024, San Francisco (CA, USA), United States. 10.1109/IPDPSW63119.2024.00171. hal-04606388v2 view on HAL pdf
- Barbara Gendron, Gaël Guibon. SEC : contexte émotionnel phrastique intégré pour la reconnaissance émotionnelle efficiente dans la conversation. 35èmes Journées d'Études sur la Parole (JEP 2024) 31ème Conférence sur le Traitement Automatique des Langues Naturelles (TALN 2024) 26ème Rencontre des Étudiants Chercheurs en Informatique pour le Traitement Automatique des Langues (RECITAL 2024), Jul 2024, Toulouse, France. pp.219-233. hal-04623019 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
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CNRS
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UniversitiesUniversité Grenoble Alpes, Grenoble INP |
Regional councilsAquitaine |
