Grid5000:Home: Difference between revisions
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Key features: | 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 | * 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 2924 overall):
- Georges da Costa. Hardware and application aware performance, power and energy models for modern HPC servers with DVFS. Sustainable Computing : Informatics and Systems, 2025, 46, pp.101106. 10.1016/j.suscom.2025.101106. hal-04983485 view on HAL pdf
- Sofya Dymchenko, Abhishek Purandare, Bruno Raffin. MelissaDL x Breed: Towards Data-Efficient On-line Supervised Training of Multi-parametric Surrogates with Active Learning. AI4S 2024 - 5th Workshop on artificial intelligence and machine learning for scientific applications, Nov 2024, Atlanta (Georgia), United States. pp.1-9. hal-04712480 view on HAL pdf
- Fatmir Asllanaj, Sylvain Contassot-Vivier, Guilherme C Fraga, Francis H.R. França, Roberta J.C. da Fonseca. New gas radiation model of high accuracy based on the principle of weighted sum of gray gases. Journal of Quantitative Spectroscopy and Radiative Transfer, 2024, 315, pp.108887. 10.1016/j.jqsrt.2023.108887. hal-04375649 view on HAL pdf
- Houssam Elbouanani, Chadi Barakat, Walid Dabbous, Thierry Turletti. Fidelity-aware Large-scale Distributed Network Emulation. Computer Networks, 2024, 10.1016/j.comnet.2024.110531. hal-04591699 view on HAL pdf
- Arun Thangamani. Optimized code generation of parallel and polyhedral loop nests using MLIR. Computer Science cs. Université de Strasbourg, 2024. English. NNT : 2024STRAD058. tel-04718259v2 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 |
