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Grid'5000

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:

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
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 2026-05-08 22:11): 5 current events, 4 planned (details)

Random pick of publications

Five random publications that benefited from Grid'5000 (at least 2943 overall):

Maxime Gobert. Contributions to the Analysis and Design of Parallel Batched Bayesian Optimization Algorithms. Operations Research math.OC. Université de Mons (Belgique), 2024. English. NNT : . tel-04801888 view on HAL pdf
Danilo Carastan-Santos, Georges da Costa, Igor Fontana de Nardin, Millian Poquet, Krzysztof Rzadca, et al.. Scheduling with lightweight predictions in power-constrained HPC platforms. IEEE Transactions on Parallel and Distributed Systems, 2025, pp.1-12. 10.1109/TPDS.2025.3586723. hal-04747713v3 view on HAL pdf
Gustavo Salazar-Gomez, Wenqian Liu, Manuel Alejandro Diaz-Zapata, David Sierra González, Christian Laugier. TLCFuse: Temporal Multi-Modality Fusion Towards Occlusion-Aware Semantic Segmentation. IV 2024 - 35th IEEE Intelligent Vehicles Symposium, Jun 2024, Jeju Island, South Korea. pp.2110-2116, 10.1109/IV55156.2024.10588460. hal-04717193 view on HAL pdf
Thomas Stavis, Laurent Lefèvre, Anne-Cécile Orgerie. Intel RAPL - Ses impacts sur l’infrastructure et son utilisation comme levier énergétique. 2026. hal-05548910 view on HAL pdf
Bruno Lévy, Nicolas Ray, Quentin Merigot, Hugo Leclerc. Large-scale semi-discrete optimal transport with distributed Voronoi diagrams. Journal of Computational Physics, 2025, pp.114374. 10.1016/j.jcp.2025.114374. hal-05253830 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

@@ Line 7: / Line 7: @@
 Key features:
-* provides '''access to a large amount of resources''': 15000 cores, 800 compute-nodes grouped in homogeneous clusters, and featuring various technologies: GPU, SSD, NVMe, 10G and 25G Ethernet, Infiniband, Omni-Path
+* 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
 * '''advanced monitoring and measurement features for traces collection of networking and power consumption''', providing a deep understanding of experiments