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* [[Media:Grid5000.pdf|Presentation of Grid'5000]] (April 2019) | * [[Media:Grid5000.pdf|Presentation of Grid'5000]] (April 2019) | ||
* [https://www.grid5000.fr/mediawiki/images/Grid5000_science-advisory-board_report_2018.pdf Report from the Grid'5000 Science Advisory Board (2018)] | * [https://www.grid5000.fr/mediawiki/images/Grid5000_science-advisory-board_report_2018.pdf Report from the Grid'5000 Science Advisory Board (2018)] | ||
* Grid'5000 is merging with [https://fit-equipex.fr FIT] to build the SILECS Infrastructure for Large-scale Experimental Computer Science. Read [http://www.silecs.net/wp-content/uploads/2018/04/Desprez-SILECS.pdf an Introduction to SILECS] (April 2018) | * Grid'5000 is merging with [https://fit-equipex.fr FIT] to build the [http://www.silecs.net/ SILECS Infrastructure for Large-scale Experimental Computer Science]. Read [http://www.silecs.net/wp-content/uploads/2018/04/Desprez-SILECS.pdf an Introduction to SILECS] (April 2018) | ||
Older documents: | Older documents: | ||
Revision as of 10:45, 24 May 2019
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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 3007 overall):
- Enzo Isnard, Sébastien Héron, Stéphane Lanteri, Mahmoud Elsawy. Hybrid model to simulate optical systems combining metasurfaces and classical refractive elements. Optics Express, 2025, 10.1364/OE.580729. hal-05290353v2 view on HAL pdf
- Thomas Bouvier. Distributed Rehearsal Buffers for Continual Learning at Scale. Machine Learning cs.LG. INSA de Rennes, 2024. English. NNT : 2024ISAR0011. tel-04986111 view on HAL pdf
- Louis Roussel. Integral equations modelling and deep learning. Machine Learning cs.LG. Université de Lille, 2025. English. NNT : 2025ULILB033. tel-05567799v2 view on HAL pdf
- Daniel Richards Arputharaj, Charlotte Rodriguez, Angelo Rodio, Giovanni Neglia. Green Federated Learning via Carbon-Aware Client and Time Slot Scheduling. MASCOTS 2025 - 33rd International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication System, Oct 2025, Paris, France. 10.1109/MASCOTS67699.2025.11283314. hal-05423023 view on HAL pdf
- Jérémie Rodez, Patricia Stolf, Thierry Monteil. Placement of Distributed Machine Learning Services for AI- and Smart Grid-Enabled IoT Platforms. 2026. hal-05279358 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 |