Creating a GPGPU-accelerated framework for pattern matching using a case study

Abstract

Nowadays, general purpose personal computers often contain a separated GPU card. The card can be used to extend the computing power of the CPU. This possibility is getting bigger and bigger focus in several areas such as bioinformatics or audio signal processing. Our goal is to build a heterogeneous GPU-CPU based framework which can search for user defined patterns in a domain-specific model. Efficient pattern matching is useful in various fields, for example in refactoring software systems, or in financial analysis applications. We started building the framework by defining and solving a simple case study to analyze the difficulties in the field and find the keys of success based on a practical example. Several challenges were faced and solved including performance and scalability. At the end, we gained enough experience to create a robust and performant framework. The paper presents the case study, its solution and the architecture of our general, GPU-based pattern matching framework.