Massive Parallelization of Real-World Automotive Real-Time Software by GPGPU

General Purpose Computing on GPU (GPGPU) is a method that allows to reduce an applications Worst Case Execution Time (WCET) through massively parallel execution and thereby increasing its real-time capability. The integration of GPUs is relevant in automotive engineering, especially in advanced driver assistance systems.We herewith share the knowledge obtained from massively parallelizing automotive real-world applications in the powertrain domain. GPGPU in this sector is up to now an insufficiently explored field in literature. For this purpose we parallelized two CPU-intense real-time applications, achieving a WCET speedup of up to 6.7 under realistic circumstances. Furthermore, we present several powertrain related key issues regarding massively parallel approaches, identified through our investigations.