Grid5000:Home
|
Grid'5000 is a precursor infrastructure of SLICES-FR, the French node of SLICES-RI, Scientific Large Scale Infrastructure for Computing/Communication Experimental Studies.
|
|
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, Big Data and AI. Key features:
Older documents:
|
Most recent news
Dear users, Due to multiple occurences of bad usages of the access machine, the technical team has decided to change the SSH connection mode to the...
Dear users, We are pleased to announce that Debian 13 (Trixie) std environment is now the default environment on most nodes. See `kaenv3 -l debian%...
Dear users, The default standard environment has changed to Debian 13 for some selected clusters over the past few weeks. Here they are: Hello everyone, Let's start with a quick TLDR, details on the rationale and implementation are available below: new modules will be available with ... |
Random pick of publications
Five random publications that benefited from Grid'5000 (at least 3048 overall):
- Samuel Pélissier, Abhishek Kumar Mishra, Mathieu Cunche, Vincent Roca, Didier Donsez. Efficiently linking LoRaWAN identifiers through multi-domain fingerprinting. Pervasive and Mobile Computing, 2025, 112, pp.102082. 10.1016/j.pmcj.2025.102082. hal-05120767 view on HAL pdf
- Jan Aalmoes. Intelligence artificielle pour des services moraux : Concilier équité et confidentialité. Intelligence artificielle cs.AI. INSA de Lyon, 2024. Français. NNT : 2024ISAL0126. tel-05014177 view on HAL pdf
- Nasser-Eddine Monir, Paul Magron, Romain Serizel. A Phoneme-Scale Assessment of Multichannel Speech Enhancement Algorithms. Trends in Hearing, 2024, 28, 10.1177/23312165241292205. hal-04854449 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
- 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
Grid'5000 sites
Current funding
INRIA |
CNRS |
UniversitiesIMT Atlantique |
Regional councilsAquitaine |

