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Automatic parallelization and scheduling approaches for co-simulation of numerical models on multi-core processors

Abstract : When designing cyber-physical systems, engineers have to integrate models from different modeling environments in order to simulate the whole system and estimate its global performances. If some parts of the system are available, it is possible to connect these parts to the simulation in a Hardware-in-the-Loop (HiL) approach. In this case, the simulation has to be performed in real-time where models periodically react to the real components. The increase of requirements on the simulation accuracy and its validity domain requires more complex models. Using such models, it becomes hard to ensure fast or real-time execution without using multiprocessor architectures. FMI (Functional Mocked-up Interface), a standard for model exchange and co-simulation, offers new opportunities for multi-core execution of models. One goal of this thesis is the extraction of potential parallelism in a set of interconnected multi-rate models. We build on the RCOSIM approach which allows the parallelization of FMI models. In the first part of the thesis, improvements have been proposed to overcome the limitations of RCOSIM. We propose new algorithms in order to allow handling multi-rate models and schedule them on multi-core processors. The improvements allow handling specific constraints such as mutual exclusion and real-time constraints. Second, we propose algorithms for the allocation and scheduling of co-simulations, taking into account different constraints. These algorithms aim at accelerating the execution of the co-simulation or ensuring its real-time execution in a HiL approach. The proposed solutions have been tested on synthetic co-simulations and validated against an industrial use case.
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Submitted on : Monday, May 6, 2019 - 1:25:43 PM
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  • HAL Id : tel-02121071, version 2


Salah Eddine Saidi. Automatic parallelization and scheduling approaches for co-simulation of numerical models on multi-core processors. Modeling and Simulation. Sorbonne Université, 2018. English. ⟨NNT : 2018SORUS036⟩. ⟨tel-02121071v2⟩



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