Towards a model transformation tool on the top of the OpenCL framework

Nowadays, applications must often handle a large amount of data and apply complex algorithms on it. It is a promising and popular way to apply the computation in parallel in order to meet the performance requirements. Since GPUs are designed to apply highly parallel computations efficiently, using CPU+GPU heterogeneous architecture have gained an increasing popularity in computation intensive applications. Model-driven development (MDE) is a widely used software development methodology in the software industry. MDE is heavily building on model transformations in converting and processing the models. Graph transformation-based model transformation is a popular technique in this field. It is based on isomorphic subgraphs matching, which often require serious computing power. Currently, model transformation tools are not capable of using the computation power of the GPUs. Our research goal is to create a general model matching and later a model transformation solution, which can take the advantages of the computation power of the GPUs. We are now focusing on pattern matching of the transformations. We would like to create a general solution which is independent of the hardware vendor; therefore, our method is based on the OpenCL framework. The novelty of this paper is a GPGPU-based pattern matching tool and some accelerating techniques to achieve faster computation. In this paper we present an overview of the solution and test results based on one of the biggest freely available movie database (IMDb). The main properties such as the performance and the scalability are discussed. The applied architecture and the steps towards the final solution are also included in the paper.