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Revision as of 08:25, 17 September 2018

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:

Presentation of Grid'5000 during the 1st Grid'5000-FIT school (April 2018)
Report from the Grid'5000 Science Advisory Board (2018)
Grid'5000 is merging with FIT to build the SILECS Infrastructure for Large-scale Experimental Computer Science. Read an Introduction to SILECS (April 2018)

Older documents:

https://www.grid5000.fr/slides/2014-09-24-Cluster2014-KeynoteFD-v2.pdf Slides from Frederic Desprez's keynote at IEEE CLUSTER 2014]
Report from the Grid'5000 Science Advisory Board (2014)

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 2026-02-14 14:09): 4 current events, 4 planned (details)

Random pick of publications

Five random publications that benefited from Grid'5000 (at least 2934 overall):

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
Maurice Brémond, Hugo Brunie, Laurent Debreu, Rupert W Ford, Florian Lemarié, et al.. Poseidon: A Source-to-Source Translator for Holistic HPC Optimizations of Ocean Models on Regular Grids. SC 2024 - International Conference for High Performance Computing, Networking, Storage, and Analysis, Nov 2024, Atlanta (Georgia), United States. , pp.1-1, 2024, 10.5281/zenodo.11190458. hal-04811677 view on HAL pdf
Cherif Latreche, Nikos Parlavantzas, Hector A Duran-Limon. FoRLess: A Deep Reinforcement Learning-based approach for FaaS Placement in Fog. UCC 2024 - 17th IEEE/ACM International Conference on Utility and Cloud Computing, Dec 2024, Sharjah, United Arab Emirates. pp.1-9. hal-04791252 view on HAL pdf
Alaaeddine Chaoub. Deep learning representations for prognostics and health management. Computer Science cs. Université de Lorraine, 2024. English. NNT : 2024LORR0057. tel-04687618 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
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''': 1000 nodes, 8000 cores, grouped in homogeneous clusters, and featuring various technologies: 10G Ethernet, Infiniband, GPUs, Xeon PHI
+* 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