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.

There exist numerous approaches to index either spatio-temporal or high-dimensional data. None of them is able to efficiently index hybrid data types, thus spatio-temporal and high-dimensional data. As the best high-dimensional indexing techniques are only able to index point-data and not now-relative data and the best spatio-temporal indexing techniques suffer from the curse of dimensionality, this thesis introduces the Spatio-Temporal Pyramid Adapter (STPA). The STPA maps spatio-temporal data on points, now-values on the median of the data set and indexes them with the pyramid technique. For high-dimensional and spatio-temporal index structures no generally accepted benchmark exists. Most index structures are only evaluated by custom benchmarks and compared to a tiny set of competitors. Benchmarks may be biased as a structure may be created to perform well in a certain benchmark or a benchmark does not cover a certain speciality of the investigated structures. In this thesis, the Interface Based Performance Comparison (IBPC) technique is introduced. It automatically generates test sets with a high code coverage on the system under test (SUT) on the basis of all functions defined by a certain interface which all competitors support. Every test set is performed on every SUT and the performance results are weighted by the achieved coverage and summed up. These weighted performance results are then used to compare the structures. An implementation of the IBPC, the Performance Test Automation Framework (PTAF) is compared to a classic custom benchmark, a workload generator whose parameters are optimized by a genetic algorithm and a specific PTAF alternative which incorporates the specific behavior of the systems under test. This is done for a set of two high-dimensional spatio-temporal indices and twelve variants of the R-tree. The evaluation indicates that PTAF performs at least as good as the other approaches in terms of minimal test cases with a maximized coverage. Several case studies on PTAF demonstrate its widespread abilities.