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On the concurrent execution of model transformations with Linda

Nowadays there exists a wide variety of model transformation languages. However, all of them present limitations, mainly performance issues, when the complexity and size of model transformations and models grow. The problems arise due to the in-memory allocation of large models as well as the time taken by the execution engines for producing the output models. This restricts the benefits of using model transformations in different application fields of model engineering where the complexity of the transformation tasks exceeds the capabilities of sequential execution engines. In this paper we tackle these limitations by introducing concurrency for model transformations to effectively improve the execution performance. Instead of reinventing the wheel, we base our approach on Linda, a mature coordination language for parallel processes. We explore how model transformations fit into Linda and show a set of basic mechanisms to enable concurrent model transformations. Initial results of applying our approach show a great potential of using Linda to improve the execution performance with respect to existing approaches.