GPU Support for Component-based Development of Embedded Systems

One pressing challenge of many modern embedded systems is to successfully deal with the considerable amount of data that originates from the interaction with the environment. A recent solution comes from the use of GPUs. Equipped with a parallel execution model, the GPU excels in parallel processing applications, providing an improved performance compared to the CPU. Another trend in the embedded systems domain is the use of component-based development. This software engineering paradigm that promotes construction of applications through the composition of software components, has been successfully used in the development of embedded systems. However, the existing approaches provide no specific support to develop embedded systems with GPUs. As a result, components with GPU capability need to encapsulate all the required GPU information in order to be successfully executed by the GPU. This leads to component specialization to specific platforms, hence drastically impeding component reusability. Our main goal is to facilitate component-based development of embedded systems with GPUs. We introduce the concept of flexible component which increases the flexibility to design embedded systems with GPUs, by allowing the system developer to decided where to place the component, i.e., either on the CPU or GPU. Furthermore, we provide means to automatically generate the required information for flexible components corresponding to their hardware placement, and to improve component communication. Through the introduced support, components with GPU capability are platform-independent, being capable to be executed on a large variety of hardware (i.e., platforms with different GPU characteristics). Furthermore, an optimization step is introduced, which groups connected flexible components into single entities that behave as regular components. Dealing with components that can be executed either by the CPU or GPU, we also introduce an allocation optimization method. The proposed solution, implemented using a mathematical solver, offers alternative options in optimizing particular system goals (e.g., memory and energy usage)