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Grid'5000

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:

provides access to a large amount of resources: 12000 cores, 800 compute-nodes grouped in homogeneous clusters, and featuring various technologies: 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
advanced monitoring and measurement features for traces collection of networking and power consumption, providing a deep understanding of experiments
designed to support Open Science and reproducible research, with full traceability of infrastructure and software changes on the testbed
a vibrant community of 500+ users supported by a solid technical team

Read more about our teams, our publications, and the usage policy of the testbed. Then get an account, and learn how to use the testbed with our Getting Started tutorial and the rest of our Users portal.

Grid'5000 is merging with FIT to build the SILECS Infrastructure for Large-scale Experimental Computer Science. Read an Introduction to SILECS (April 2018)

Recently published documents and presentations:

Presentation of Grid'5000 (April 2019)
Report from the Grid'5000 Science Advisory Board (2018)

Older documents:

Grid'5000 is supported by a scientific interest group (GIS) hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations. Inria has been supporting Grid'5000 through ADT ALADDIN-G5K (2007-2013), ADT LAPLACE (2014-2016), and IPL HEMERA (2010-2014).

Current status (at 2026-09-21 17:15): 5 current events, None planned (details)

Random pick of publications

Five random publications that benefited from Grid'5000 (at least 3007 overall):

François Lemaire, Louis Roussel. Deep Learning for Integro-Differential Modelling. RISC Proceedings on Symbolic Computation and Machine Learning, Johannes Kepler Universität Linz, 2026, Mar 2026, Hagenberg Castle, Austria. 10.35011/risc-proceedings-scml.2. hal-05230281v3 view on HAL pdf
Alix Tremodeux, Guillaume Pallez, Erven Rohou. How to determine a machine's true age in an HPC system?. JLESC 2026 -18th workshop of Joint Laboratory for Extreme Scale Computing, May 2026, Julich, Germany. 2026. hal-05662829 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
Antoine Franchini, Stéphane Girard, Anne Dutfoy. Adaptive confidence intervals for extreme quantiles from heavy-tailed distributions. Statistics and Computing, 2026, 36, pp.175. 10.1007/s11222-026-10930-9. hal-05322341v3 view on HAL pdf
Rahma Hellali, Zaineb Chelly Dagdia, Karine Zeitouni. A Multi-Objective Multi-Agent Interactive Deep Reinforcement Learning Approach for Feature Selection. International conference on neural information processing, Dec 2024, Auckland (Nouvelle Zelande), New Zealand. pp.15. hal-04723314 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

Universities

Université Grenoble Alpes, Grenoble INP
Université Rennes 1, Rennes
Institut National Polytechnique de Toulouse / INSA / FERIA / Université Paul Sabatier, Toulouse
Université Bordeaux 1, Bordeaux
Université Lille 1, Lille
École Normale Supérieure, Lyon

Regional councils

Aquitaine
Auvergne-Rhône-Alpes
Bretagne
Champagne-Ardenne
Provence Alpes Côte d'Azur
Hauts de France
Lorraine