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 2928 overall):
- Guillaume Rosinosky, Donatien Schmitz, Etienne Rivière. StreamBed: Capacity Planning for Stream Processing. DEBS 2024 - 18th ACM International Conference on Distributed and Event-based Systems, Jun 2024, Lyon, France. pp.90-102, 10.1145/3629104.3666034. hal-04708354 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
- Houssem Ouertatani. Efficient Deep Neural Architecture Search via Bayesian Optimization : An application to Computer Vision. Computer Vision and Pattern Recognition cs.CV. Université de Lille, 2024. English. NNT : 2024ULILB044. tel-05014154 view on HAL pdf
- Adrien Schoen, Gregory Blanc, Pierre-François Gimenez, Yufei Han, Frédéric Majorczyk, et al.. A tale of two methods: unveiling the limitations of GAN and the rise of bayesian networks for synthetic network traffic generation. 2024 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), Jul 2024, Vienna, Austria. pp.273-286, 10.1109/EuroSPW61312.2024.00036. hal-04871298 view on HAL pdf
- Dorian Goepp, Fernando Ayats Llamas, Olivier Richard, Quentin Guilloteau. ACM REP24 Tutorial: Reproducible distributed environments with NixOS Compose. 2024, pp.1-3. hal-04613983 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 |
