ParSys

Parallel Systems- ParSys

The ParSys team is specialized in high performance computing and the theoretical and implementation aspects of distributed algorithms.

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

  • Thèse

    Hugo Gabrielidis. High-performance machine learning and data analysis for next-generation railway design. Artificial Intelligence [cs.AIArtificial Intelligence]. Université Paris-Saclay, 2026. English. ⟨NNT : 2026UPAST019⟩. ⟨tel-05555019⟩

    ParSys

    Year of publication

    Available in free access

  • Thèse

    Brice Chichereau. Optimization and evaluation of integrated HPC-Quantum software stacks. Computer Science [cs]. Université Paris-Saclay, 2026. English. ⟨NNT : 2026UPASG008⟩. ⟨tel-05551876⟩

    ParSys, ParSys

    Year of publication

    Available in free access

  • Communication dans un congrès, Communication dans un congrès

    Julien Rauch, Damien Rontani, Stéphane Vialle. Towards a Quantum Generative Graph-Based Clustering for Molecule Discovery. Quest-IS, Dec 2025, Palaiseau, France. pp.243-251, ⟨10.1007/978-3-032-13855-2_22⟩. ⟨hal-05549507⟩

    ParSys

    Year of publication

  • Article dans une revue

    Amanda Candemil Kanashiro, Hugo Gabrielidis, Filippo Gatti, Manoel Damião Sousa-Neto. Impact of artifact reduction using generative adversarial networks on diagnostic accuracy in cone-beam computed tomography. Journal of Dentistry, 2026, 167, pp.106400. ⟨10.1016/j.jdent.2026.106400⟩. ⟨hal-05549512⟩

    ParSys

    Year of publication

    Available in free access

  • Thèse

    Atte Torri. Towards a fast task-based parallel tensor solver for high-dimensional problems. Numerical Analysis [cs.NA]. Université Paris-Saclay, 2025. English. ⟨NNT : 2025UPASG106⟩. ⟨tel-05534633⟩

    ParSys, ParSys

    Year of publication

    Available in free access

  • Article dans une revue

    Hugo Gabrielidis, Filippo Gatti, Stéphane Vialle. Physics-based super-resolved simulation of 3D elastic wave propagation adopting scalable diffusion transformer. Computer Physics Communications, 2026, 320, pp.109930. ⟨10.1016/j.cpc.2025.109930⟩. ⟨hal-05485963⟩

    ParSys

    Year of publication

    Available in free access

  • Communication dans un congrès

    Joseph Touzet, Oguz Kaya, Pablo Arrighi, Amélia Durbec. QUIDS: A Large-Scale Distributed Framework for Quantum Irregular Dynamics Simulations. Q-CASA 2025 – IPDPS Workshop on Quantum Computing Algorithms, Systems, and Applications, Jun 2025, Milan, Italy. pp.491-500, ⟨10.1109/IPDPSW66978.2025.00080⟩. ⟨hal-05472605⟩

    ParSys

    Year of publication

    Available in free access

  • Communication dans un congrès

    Brice Chichereau, Stéphane Vialle, Miwako Tsuji, Patrick Carribault, Mitsuhisa Sato. HPCQCMark: a new modular HPC-QC benchmarking framework. 2025 IEEE International Conference on Quantum Computing and Engineering (QCE), Aug 2025, Albuquerque, United States. pp.8-14, ⟨10.1109/QCE65121.2025.10285⟩. ⟨hal-05426530⟩

    ParSys

    Year of publication

    Available in free access

  • Communication dans un congrès

    Pierre Fraigniaud, Minh Hang Nguyen, AmiArchitectures et modèles pour l'Interaction Paz. A Simple Lower Bound for Set Agreement in Dynamic Networks. 2025 Symposium on Simplicity in Algorithms (SOSA), Jan 2025, New Orleans, United States. pp.253-262, ⟨10.1137/1.9781611978315.20⟩. ⟨hal-05403931⟩

    ParSys

    Year of publication

    Available in free access

  • Traduction

    Pierre Jehel, Stéphane Vialle. Collaborative Platform for Railway Projects – Business Needs Analysis and Their Formalization as Functional Requirements. 2023. ⟨hal-05371720⟩

    ParSys

    Year of publication

    Available in free access