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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: 1000 nodes, 8000 cores, grouped in homogeneous clusters, and featuring various technologies: 10G Ethernet, Infiniband, Omnipath, GPUs, Xeon PHI
  • 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.


Recently published documents and presentations:

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 2026-11-01 09:01): 7 current events, 2 planned (details)


Random pick of publications

Five random publications that benefited from Grid'5000 (at least 3010 overall):

  • Ishaan Shah, S Lucas, G Drettakis. Frequency-Aware Spatial-Angular Gaussians for Efficient Global Illumination Precomputation and Real-Time Rendering. Computer Graphics Forum, In press, 45 (4), 10.1111/cgf.70542. hal-05702737 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
  • Cédric Prigent, Melvin Chelli, Alexandru Costan, Loïc Cudennec, René Schubotz, et al.. Efficient Resource-Constrained Federated Learning Clustering with Local Data Compression on the Edge-to-Cloud Continuum. HiPC 2024 - 31st IEEE International Conference on High Performance Computing, Data, and Analytics, Dec 2024, Bengaluru (Bangalore), India. pp.1-11, 10.1109/HiPC62374.2024.00033. hal-04779813 view on HAL pdf
  • Hadrien Crassous, Mohamed Yassine Kabouri, Minahil Raza, Joni Pajarinen, Riad Akrour. Embodied Active Learning under Limited Annotation and Navigation Budget for Object Detection. IROS 2026 - IEEE RSJ International Conference on Intelligent Robots and Systems, Sep 2026, Pittsburg, United States. hal-05701172v2 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


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

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