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Revision as of 21:53, 19 January 2020

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: 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 2025-09-19 00:03): 5 current events, 2 planned (details)

Random pick of publications

Five random publications that benefited from Grid'5000 (at least 2922 overall):

Maël Madon. Digital Sufficiency in Data Centers : Studying the Impact of User Behaviors. Computer Science cs. Université de Toulouse, 2024. English. NNT : 2024TLSES046. tel-04675558 view on HAL pdf
Wedan Emmanuel Gnibga, Anne Blavette, Anne-Cécile Orgerie. Renewable Energy in Data Centers: the Dilemma of Electrical Grid Dependency and Autonomy Costs. IEEE Transactions on Sustainable Computing, 2024, 9 (3), pp.315-328. 10.1109/TSUSC.2023.3307790. hal-04189173 view on HAL pdf
Edouard Guégain. Configuration-driven Software Optimization. Data Structures and Algorithms cs.DS. Université de Lille, 2023. English. NNT : 2023ULILB020. tel-04350545v2 view on HAL pdf
Prerak Srivastava. Realism in virtually supervised learning for acoustic room characterization and sound source localization. Machine Learning cs.LG. Université de Lorraine, 2023. English. NNT : 2023LORR0184. tel-04313405 view on HAL pdf
Marc Jourdan, Clémence Réda. An Anytime Algorithm for Good Arm Identification. 2024. hal-04688141 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''': 12000 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: 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