Programming Embedded Manycore: Refinement and Optimizing Compilation of a Parallel Action Language for Hierarchical State Machines

  • Ivan Llopard. Programming Embedded Manycore: Refinement and Optimizing Compilation of a Parallel Action Language for Hierarchical State Machines. PhD thesis, 2016.
    [BibTeX] [Abstract]

    Modeling languages propose convenient abstractions and transformations to handle the complexity of today’s embedded systems. Based on the formalism of Hierarchical State Machine, they enable the expression of hierarchical control parallelism. However, they face two importants challenges when it comes to model data-intensive applications: no unified approach that also accounts for data-parallel actions; and no effective code optimization and generation flows. In this thesis, we propose a modeling language extended with parallel action semantics and hierarchical indexed-state machines suitable for computationally intensive applications. Together with its formal semantics, we present an optimizing model compiler aiming for the generation of efficient data-parallel implementations.

    @PhdThesis{2016-04-LLOPARD,
    author = {Ivan Llopard},
    title = {{Programming Embedded Manycore: Refinement and Optimizing Compilation of a Parallel Action Language for Hierarchical State Machines}},
    institution = {Universit\'e Pierre \& Marie Curie, Paris},
    date = {2016-04-26},
    year = {2016},
    abstract = {Modeling languages propose convenient abstractions and transformations to handle the complexity of today's embedded systems. Based on the formalism of Hierarchical State Machine, they enable the expression of hierarchical control parallelism. However, they face two importants challenges when it comes to model data-intensive applications: no unified approach that also accounts for data-parallel actions; and no effective code optimization and generation flows. In this thesis, we propose a modeling language extended with parallel action semantics and hierarchical indexed-state machines suitable for computationally intensive applications. Together with its formal semantics, we present an optimizing model compiler aiming for the generation of efficient data-parallel implementations.}
    }

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