Towards creating a GPGPU-accelerated framework for pattern matching

Abstract

Model-driven engineering (MDE) is a popular software development methodology in the software industry. Finding a predefined pattern in a domain-specific model can be requested in MDE. This technique can help in optimizing or refactoring the models or to translate from one language to another one. The goal of the current researching is to create a framework for MDE which can find patterns defined by the users. Performance is a key issue. Using heterogeneous computation system (e.g.: CPU+GPU) is a promising way to increase the performance of the calculation. Therefore, we created a solution based on the OpenCL framework which is one of the most popular heterogeneous platforms. In this paper, the new pattern matching framework and the main steps of its creation are presented. The applied conception consists of two main steps. Firstly, a simpler case study is solved and experiences are collected from the occurring challenges. Secondly, the achieved solution was extended for general pattern matching. In both steps, the core algorithms are implemented according to the test-driven development methodology. To elaborate these steps, a new technique is provided which can be useful in creating any GPU-based model transformation and thus MDE approaches are improved in general.