On the Concurrent Execution of Model Transformations with Linda

Nowadays there exists a wide variety of model transforma-tion 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 concur-rency for model transformations to effectively improve the execution performance. Instead of reinventing the wheel, we base our approach on Linda, a mature coordination lan-guage for parallel processes. We explore how model trans-formations 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.Ministerio de Ciencia e Innovación TIN2011-23795Ministerio de Economía y Competitividad TIN2011-15497-

DC4MT : uma abordagem orientada a dados para transformação de modelos

Orientador: Marcos Didonet Del FabroTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Programa de Pós-Graduação em Informática. Defesa : Curitiba, 25/08/2020Inclui referências: p. 95-106Área de concentração: Ciência da ComputaçãoResumo: Transformações de Modelos são operações que recebem um conjunto de modelos como entrada e produzem um conjunto de modelos como saída, seguindo uma especificação. Há uma coleção diversificada de abordagens e ferramentas utilizadas para a especificação de diferentes tipos de transformações de modelos. A maioria dessas abordagens adota como estratégia a execução local e sequencial. No entanto, essas abordagens não estão totalmente aptas para processar modelos com grandes quantidades de elementos. VLMs (Very Large Models) são modelos que possuem milhões de elementos. Esses modelos estão presentes em domínios de aplicações como na indústria automotiva, modernização de sistemas legados, internet das coisas, redes sociais, entre outros domínios. Essas abordagens possuem lacunas para suportar o processamento desses VLMs. Por exemplo, para possibilitar a execução das transformações de modelos, considerando a escala do problema ou para melhoria de desempenho. Nesta tese é proposta a Dc4MT, uma abordagem para suportar transformações de VLMs com a aplicação e adaptação de técnicas relacionadas à distribuição de dados. A Dc4MT é uma abordagem Orientada a Dados (Dc - Data-centric) para ser aplicada no domínio da Engenharia Dirigida por Modelos (MDE - Model Driven Engineering). A abordagem é especificada, utilizando um framework de processamento distribuído, e define um conjunto de operações para a fragmentação, extração e transformação de modelos. A fragmentação é uma operação que divide os modelos de entrada (em formatos XMI ou JSON) em fragmentos, de modo que esses fragmentos possam ser distribuídos e processados de maneira paralela/distribuída. A extração é uma operação que processa os fragmentos do modelo de entrada e os traduz em um grafo acíclico, atribuindo um novo domínio de modelagem a esses fragmentos. A transformação de modelos na abordagem Dc4MT é uma operação que transforma modelos de entrada em modelos de saída (M2M) a partir do resultado da extração. As execuções de transformação podem ser em modo paralelo ou distribuído, com ou sem a intervenção no método de particionamento do framework disponível para melhorar o desempenho. Um conjunto de modelos de entrada (datasets) e os ambientes local (transformações paralelas) e distribuído (transformações distribuídas) são utilizados nos experimentos para validar a abordagem Dc4MT, sob os aspectos de factibilidade, desempenho e de escalabilidade. Os resultados desses experimentos, mostram que as operações de fragmentação e extração de modelos favorecem a transformação escalável de VLMs, reconstruindo a estrutura dos fragmentos em um grafo. A operação de extração é executada em modo paralelo/distribuído. Além disso, os aspectos como a imutabilidade, lazy-evaluate e o paralelismo implícito presentes na Dc4MT, permitem o processamento paralelo/distribuído de regras de transformação em uma plataforma escalável. Palavras-chave: Abordagem Orientada a Dados. Engenharia Dirigida por Modelos. Transformação Paralela de Modelos. Transformação Distribuída de Modelos.Abstract: Model Transformations are operations that receive a set of source models as input and produce a set of target models as output, following a specification. There is a variety of approaches and tools used for the specification of different types of model transformation. Most of these approaches adopt for model transformation the local and sequential execution strategy. However, these approaches not fully adapted for processing models with large amounts of elements. VLMs (Very Large Models) are models with millions of elements. These models are present in application domains such as the automotive industry, modernization of legacy systems, internet of things, social networks, among others. These approaches have gaps to support the processing of these increasingly larger models. For example, to enable model transformations, considering the scale of the problem or to improve performance. In this thesis, the Dc4MT is proposed such as an approach to support transformation of VLMs, applying and adapting distribution techniques of data. The Dc4MT is a Data-centric (Dc) approach for applying in Model Driven Engineering (MDE). The approach will be specified using a distributed processing framework, and defines a set of operations for fragmentation, extraction, and transformation of models. The fragmentation is an operation that splits the input models (in the XMI or JSON formats) in a way that the fragments can be processed in parallel/distributed. The extraction is an operation that processes the fragments of the input model in parallel and translates them to an acyclic graph, assigning a new modeling domain to these fragments. The model transformation in Dc4MT is an operation that transforms input models in output models (M2M) from the results of the extraction. The transformation executions can be parallel or distributed with ou without the intervention in the framework partitioning method to improve the performance. A set of input models (datasets) and the local (parallel transformations) and distributed (distributed transformations) environments are used in the experiments to validate the Dc4MT approach, in terms of feasibility, performance, and scalability. The results of the experiments show that the model fragmentation and extraction operations favor the scalable transformation of models, reconstructing the structure of the fragments in a graph. The extraction operation is executed on parallel/distributed way. Moreover, aspects such as immutability, lazy-evaluation, and implicit parallelism present in Dc4MT, allowing the parallel/distributed processing of transformation rules on a scalable platform. Keywords: Data-centric Approach. Model Driven Engineering. Parallel Model Transformation. Distributed Model Transformation

Parallel and Distributed Execution of Model Management Programs

The engineering process of complex systems involves many stakeholders and development artefacts. Model-Driven Engineering (MDE) is an approach to development which aims to help curtail and better manage this complexity by raising the level of abstraction. In MDE, models are first-class artefacts in the development process. Such models can be used to describe artefacts of arbitrary complexity at various levels of abstraction according to the requirements of their prospective stakeholders. These models come in various sizes and formats and can be thought of more broadly as structured data. Since models are the primary artefacts in MDE, and the goal is to enhance the efficiency of the development process, powerful tools are required to work with such models at an appropriate level of abstraction. Model management tasks – such as querying, validation, comparison, transformation and text generation – are often performed using dedicated languages, with declarative constructs used to improve expressiveness. Despite their semantically constrained nature, the execution engines of these languages rarely capitalize on the optimization opportunities afforded to them. Therefore, working with very large models often leads to poor performance when using MDE tools compared to general-purpose programming languages, which has a detrimental effect on productivity. Given the stagnant single-threaded performance of modern CPUs along with the ubiquity of distributed computing, parallelization of these model management program is a necessity to address some of the scalability concerns surrounding MDE. This thesis demonstrates efficient parallel and distributed execution algorithms for model validation, querying and text generation and evaluates their effectiveness. By fully utilizing the CPUs on 26 hexa-core systems, we were able to improve performance of a complex model validation language by 122x compared to its existing sequential implementation. Up to 11x speedup was achieved with 16 cores for model query and model-to-text transformation tasks