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 Javabased
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.
Buscar en Dimension