Grid5000:Home: Difference between revisions
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[[Image:renater5-g5k.jpg|thumbnail|250px|right|Grid'5000]] | [[Image:renater5-g5k.jpg|thumbnail|250px|right|Grid'5000]] | ||
'''Grid'5000 is a large-scale and | '''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: | Key features: | ||
* provides '''access to a large amount of resources''': | * 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 2928 overall):
- 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
- Mostafa Sadeghi, Romain Serizel. Posterior sampling algorithms for unsupervised speech enhancement with recurrent variational autoencoder. International Conference on Acoustics Speech and Signal Processing (ICASSP), IEEE, Apr 2024, Seoul (Korea), South Korea. 10.48550/arXiv.2309.10439. hal-04210679v2 view on HAL pdf
- Hee-Soo Choi, Priyansh Trivedi, Mathieu Constant, Karën Fort, Bruno Guillaume. Beyond Model Performance: Can Link Prediction Enrich French Lexical Graphs?. The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING), May 2024, Turin, Italy. hal-04537462 view on HAL pdf
- Ali Golmakani, Mostafa Sadeghi, Xavier Alameda-Pineda, Romain Serizel. A weighted-variance variational autoencoder model for speech enhancement. ICASSP 2024 - International Conference on Acoustics Speech and Signal Processing, IEEE, Apr 2024, Seoul (Korea), South Korea. pp.1-5, 10.1109/ICASSP48485.2024.10446294. hal-03833827v2 view on HAL pdf
- Alaaeddine Chaoub. Deep learning representations for prognostics and health management. Computer Science cs. Université de Lorraine, 2024. English. NNT : 2024LORR0057. tel-04687618 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 |