Constrained mixed-critical parallelization for distributed heterogeneous systems

Distributing software effectively to multi core, many core, and distributed systems has been studied for decades but still advances successively driven by domain specific constraints. Programming vehicle ECUs is one of the most constrained domains that recently approached the need for concurrency due to advanced driver assistant systems or autonomous driving approaches. In this paper, software distribution challenges for such systems are discussed and solutions are presented for instruction precise modeling, affinity constrained distribution, and reducing task response times achieved by advanced software parallelization. Therefore, existing partitioning and mapping algorithms are advanced to consider affinity constraints, software component tags and communication costs. Our experiments along a remote controlled model car show that using our new advanced results instead of sequential implementations or software distributions provided by the operating system on a distributed heterogeneous system significantly improves its responsiveness in order to potentially reduce energy consumption and replaces error prone manual constraint considerations for mixed-critical applications.