Grid5000:Home: Difference between revisions

Revision as of 14:26, 6 April 2016

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, 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:

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).

Latest publications from Grid'5000 users

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

Cédric Prigent, Alexandru Costan, Gabriel Antoniu, Loïc Cudennec. Enabling Federated Learning across the Computing Continuum: Systems, Challenges and Future Directions. Future Generation Computer Systems, 2024, 160, pp.767-783. 10.1016/j.future.2024.06.043. hal-04659211 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
Reda Khoufache, Anisse Belhadj, Mustapha Lebbah, Hanene Azzag. Distributed MCMC Inference for Bayesian Non-parametric Latent Block Model. 28th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2024, May 2024, Taipei, Taiwan. pp.271-283, 10.1007/978-981-97-2242-6_22. hal-04623748 view on HAL pdf
François Lemaire, Louis Roussel. Parameter Estimation with Integral Elimination. Differential Algebra and Related Topics XII, Apr 2024, Kassel, Germany. . hal-04576171 view on HAL pdf
Dorian Goepp, Samuel Brun, Quentin Guilloteau, Olivier Richard. Un prototype de cache de métadonnées pour le passage à l'échelle de NixOS-Compose. COMPAS 2024 - Conférence francophone d'informatique en Parallélisme, Architecture et Système, Jul 2024, Nantes, France. pp.1-8. hal-04632952 view on HAL pdf

Current status (at 2026-02-15 09:03): 4 current events, 4 planned (details)

Latest news

Grid'5000 winter school now finished

The Grid'5000 spring school took place between February 2nd, 2016 and February 5th, 2016 in Grenoblen. Two awards were given for presentation entries:

Best presentation award to Houssem-Eddine Chihoub and Christine Collet for their work on A scalability comparaison study of smart meter data management approaches (slides)

Most promising experiment to Anna Giannakou for her work Towards Self Adaptable Security Monitoring in IaaS Clouds '(https://www.grid5000.fr/school2016/g5kws16_slides/grid5k-winterschool2016.pptx slides])

10 year later, the Grid'5000 school returns to Grenoble

10 year after the first edition of the Grid'5000 school, we invite the Grid'5000 community to take part in the Grid'5000 winter School 2016, February 2-5, 2016.

Grid'5000 co-organizing the SUCCES 2015 event

These will take place November 5-6, 2015, in Paris. Dedicated to users of grid, cloud or regional HPC centers, the SUCCES 2015 days are organised by France Grilles, Grid'5000, Groupe Calcul and GDR ASR.

Grid'5000 sites

Current funding

As from June 2008, INRIA is the main contributor to Grid'5000 funding.

INRIA

CNRS

Universities

University Joseph Fourier, Grenoble
University of Rennes 1, Rennes
Institut National Polytechnique de Toulouse / INSA / FERIA / Université Paul Sabatier, Toulouse
University Bordeaux 1, Bordeaux
University Lille 1, Lille
Ecole Normale Supérieure, Lyon
Université de Reims Champagne-Ardenne, Reims

Regional councils

Aquitaine
Bretagne
Champagne-Ardenne
Provence Alpes Côte d'Azur
Nord Pas de Calais
Lorraine

@@ Line 36: / Line 36: @@
 |- valign="top"
 |width="50%" bgcolor="#f5f5f5" style="border:1px solid #cccccc;padding:1em;padding-top:0.5em;"|
-[[Image:best_presentation_award_2016_Chihoub.png|252px|Best presentation award to Houssem-Eddine Chihoub]]
+[[Image:best_presentation_award_2016_Chihoub.png|Best presentation award to Houssem-Eddine Chihoub]]
-Best presentation award to Houssem-Eddine Chihoub and Christine Collet for their work on ''A scalability comparaison study of smart meter data management approaches''
+Best presentation award to Houssem-Eddine Chihoub and Christine Collet for their work on ''A scalability comparaison study of smart meter data management approaches'' ([https://www.grid5000.fr/school2016/g5kws16_slides/grid5000-school-chihoub.pdf slides])
 |width="50%" bgcolor="#f5f5f5" style="border:1px solid #cccccc;padding:1em;padding-top:0.5em;"|
-[[Image:most_promising_experiment_Giannakou.png|252px|Most promising experiment to Anna Giannakou]]
+[[Image:most_promising_experiment_Giannakou.png|Most promising experiment to Anna Giannakou]]
-Most promising experiment to Anna Giannakou for her work ''Towards Self Adaptable Security Monitoring in IaaS Clouds''
+Most promising experiment to Anna Giannakou for her work ''Towards Self Adaptable Security Monitoring in IaaS Clouds'' '(https://www.grid5000.fr/school2016/g5kws16_slides/grid5k-winterschool2016.pptx slides])
 |-
 |}