Grid5000:Home: Difference between revisions
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Key features: | Key features: | ||
* provides '''access to a large amount of resources''': | * 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 | * '''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 | * '''advanced monitoring and measurement features for traces collection of networking and power consumption''', providing a deep understanding of experiments | ||
Revision as of 23:57, 11 February 2020
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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:
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:
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Random pick of publications
Five random publications that benefited from Grid'5000 (at least 2925 overall):
- Georges da Costa, Atom Deutsch-Filippi, Igor Fontana de Nardin, Jean-Marc Nicod, Veronika Rehn-Sonigo, et al.. Cooperative vs. non-cooperative marketplace for computing jobs to be run on geo-distributed data centers only supplied by renewable energies. Cluster Computing, 2025, 28 (10), pp.637. 10.1007/s10586-025-05300-5. hal-05239473 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
- Danilo Carastan-Santos, Georges da Costa, Millian Poquet, Patricia Stolf, Denis Trystram. Light-weight prediction for improving energy consumption in HPC platforms. Euro-Par 2024, Carretero, J., Shende, S., Garcia-Blas, J., Brandic, I., Olcoz, K., Schreiber, M., Aug 2024, Madrid, Spain. pp.152-165, 10.1007/978-3-031-69577-3_11. hal-04566184v2 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
- Wedan Emmanuel Gnibga, Anne Blavette, Anne-Cécile Orgerie. Latency, Energy and Carbon Aware Collaborative Resource Allocation with Consolidation and QoS Degradation Strategies in Edge Computing. ICPADS 2023 - IEEE International Conference on Parallel and Distributed Systems, Dec 2023, Hainan, China. pp.1-10, 10.1109/ICPADS60453.2023.00349. hal-04275783 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
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CNRS
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UniversitiesUniversité Grenoble Alpes, Grenoble INP |
Regional councilsAquitaine |
