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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
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 2026-10-25 12:47): 8 current events, 2 planned (details)
Thomas Bouvier. Distributed Rehearsal Buffers for Continual Learning at Scale. Machine Learning cs.LG. INSA de Rennes, 2024. English. NNT : 2024ISAR0011. tel-04986111 view on HALpdf
Mathieu Bacou. FaaSLoad : fine-grained performance and resource measurement for function-as-a-service. 2024. hal-04836444 view on HALpdf
Ahmed Alharbi, Charles Bouillaguet. Lower bounds on the performance of hardware circuits computing the Möbius transform. 2026. hal-04896063v2 view on HALpdf
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 HALpdf
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 HALpdf
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