Autor:
Wimmer, Manuel

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E-mails conocidos

manuel.wimmer@jku.at
wimmer@big.tuwien.ac.at

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Wimmer

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Manuel

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Afiliaciones conocidas

Johannes Kepler University Linz, Austria
Johannes Kepler University Linz
Christian Doppler Laboratory for Model-Integrated Smart Production, TU Wien
Business Informatics Group, Vienna University of Technology
CDL-Mint, Johannens Kepler University, Austria
University of Linz, Spain

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20171
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Tema: Performance ×

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  • Miniatura
    Artículo
    A Linda-based Platform for the Parallel Execution of Out-place Model Transformations
    Burgueño, Lola; Wimmer, Manuel; Vallecillo, Antonio. Actas de las XXII Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2017), 2017-07-19.
    Context: The performance and scalability of model transformations is gaining interest as industry is progressively adopting model-driven techniques and multicore computers are becoming commonplace. However, existing model transformation engines are mostly based on sequential and in-memory execution strategies, and thus their capabilities to transform large models in parallel and distributed environments are limited. Objective: This paper presents a solution that provides concurrency and distribution to model transformations. Method: Inspired by the concepts and principles of the Linda coordination language, and the use of data parallelism to achieve parallelization, a novel Java-based execution platform is introduced. It offers a set of core features for the parallel execution of out-place transformations that can be used as a target for high-level transformation language compilers. Results: Significant gains in performance and scalability of this platform are reported with regard to existing model transformation solutions. These results are demonstrated by running a model transformation test suite, and by its comparison against several state-of-the-art model transformation engines. Conclusion: Our Linda-based approach to the concurrent execution of model transformations can serve as a platform for their scalable and efficient implementation in parallel and distributed environments.