Communication dans un congrès
ParSys
Parallel Systems- ParSys
Research Themes
The team’s activity also involves the use of state-of-the-art parallel architectures to achieve optimal performance of codes developed for future exascale platforms. In addition, drawing inspiration from natural, efficient and robust phenomena, the team studies natural distributed algorithms to design efficient solutions for emerging networks, and even to develop robust distributed circuits in bacterial cell consortia, for computational or medical purposes (biological computers, smart drugs, etc.).
The main challenges concerning the algorithms studied in the ParSys team are: performance optimization, scaling and load balancing, fault tolerance and energy saving.
The applications (ranging from scientific computing, quantum computing and data analysis to network protocols and microbiological circuits) meet essential industrial or scientific needs and are the subject of industrial cooperation with ATOS-Bull, Total, and EdF, among others.
Coordination
Last publications
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Communication dans un congrès
Atte Torri, Przemysław Dominikowski, Brice Pointal, Oguz Kaya, Laércio Lima Pilla, et al.. Near-Optimal Contraction Strategies for the Scalar Product in the Tensor-Train Format. Euro-Par 2025 – 31 International European Conference on Parallel and Distributed Computing, Aug 2025, Dresden, Germany. pp.63-77, ⟨10.1007/978-3-031-99872-0_5⟩. ⟨hal-05285400⟩
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Proceedings/Recueil des communications
Quentin Delamea, Janna Burman, Jerome Gurhem, Mohamed Khairallah, Wilfried Kirschenmann, et al.. Cloud-Agnostic Serverless Platform for Fault-Tolerant Execution of Dynamic Task Graphs. 2025 IEEE Cloud Summit, Jun 2025, Washington DC, United States. IEEE, pp.39-45, 2025, ⟨10.1109/Cloud-Summit64795.2025.00014⟩. ⟨hal-05265233⟩
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Article dans une revue
Amanda Pelegrin Candemil, Hugo Gabrielidis, Filippo Gatti, Benjamin Salmon, Matheus Oliveira, et al.. Diagnostic performance of artefact-reduced cone-beam CT images using a generative adversarial neural network. Expert Systems with Applications, In press, 296 (Part B), pp.128907. ⟨10.1016/j.eswa.2025.128907⟩. ⟨hal-05157692⟩
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Article dans une revue
Pierre Fraigniaud, AmiArchitectures et modèles pour l'Interaction Paz, Sergio Rajsbaum. A speedup theorem for asynchronous computation with applications to consensus and approximate agreement. Distributed Computing, 2025, 38, pp.163-183. ⟨10.1007/s00446-025-00480-0⟩. ⟨hal-05114147⟩
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Pré-publication, Document de travail
Gabrielidis Hugo, Filippo Gatti, Vialle Stephane. Physics-Based Super-Resolved Simulation of 3D Elastic Wave Propagation Adopting Scalable Diffusion Transformer. 2025. ⟨hal-05044913⟩
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Article dans une revue, Article dans une revue
Julien Rauch, Damien Rontani, Stéphane Vialle. Data clustering on hybrid classical-quantum NISQ architecture with generative-based variational and parallel algorithms. Journal of Systems Architecture, In press, Special Issue on Architecture of Computing Systems Conference 2024, 165, pp.103431. ⟨10.1016/j.sysarc.2025.103431⟩. ⟨hal-05040633⟩
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Article dans une revue, Article dans une revue
Guanlin He, Stéphane Vialle, Marc Baboulin. Generating Sparse Matrices for Large-scale Spectral Clustering on a Single GPU. International Journal of Parallel Programming, In press, 53 (4), pp.22. ⟨10.1007/s10766-025-00799-y⟩. ⟨hal-05040711⟩
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Pré-publication, Document de travail
Niccolò Perrone, Fanny Lehmann, Hugo Gabrielidis, Stefania Fresca, Filippo Gatti. Integrating Fourier Neural Operators with Diffusion Models to improve Spectral Representation of Synthetic Earthquake Ground Motion Response. 2025. ⟨hal-05016980⟩
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Thèse
Fabricio Cravo. Design of Distributed Biological Circuits. Bioinformatics [q-bio.QM]. Université Paris-Saclay, 2024. English. ⟨NNT : 2024UPASG059⟩. ⟨tel-05007253⟩