Model-Based Exploration of Parallelism in Context of Automotive Multi-Processor Systems

This dissertation entitled ’Model-Based Exploration of Parallelism in the Context of Automotive Multi-Core Systems’ deals with the analytical investigation of different temporal relationships for automotive multi-processor systems subject to critical, embedded, real-time, distributed, and heterogeneous domain requirements. Vehicle innovation increasingly demands high-performance platforms in terms of, e.g., highly assisted or autonomous driving such that established software development processes must be examined, revised, and advanced. The goal is not to develop application software itself, but instead to improve the model-based development process, subject to numerous constraints and requirements. Model-based software development is, for example, an established process that allows systems to be analyzed and simulated in an abstracted, standardized, modular, isolated, or integrated manner. The verification of real-time behavior taking into account various constraints and modern architectures, which include graphics and heterogeneous processors as well as dedicated hardware accelerators, is one of many challenges in the real-time and automotive community. The software distribution across hardware entities and the identification of software that can be executed in parallel are crucial in the development process. Since these processes usually optimize one or more properties, they belong to the category of problems that can only be solved in polynomial time using non-deterministic methods and thus make use of (meta) heuristics for being solved. Such (meta) heuristics require sophisticated implementation and configuration, due to the properties to be optimized are usually subject to many different analyses. With the results of this dissertation, various development processes can be adjusted to modern architectures by using new and extended processes that enable future and computationally intensive vehicle applications on the one hand and improve existing processes in terms of efficiency and effectiveness on the other hand. These processes include runnable partitioning, task mapping, data allocation, and timing verification, which are addressed with the help of constraint programming, genetic algorithms, and heuristics