Response-Time Analysis of Parallel Fork-Join Workloads with Real-Time Constraints

The advent of multi- and many-core processors comes with new challenges and opportunities for the designer of embedded real-time applications. By using parallel programming techniques (e.g. OpenMP) software engineers can leverage from the available hardware parallelism and speed up the algorithms. The inherent redundancy of multi-core architectures can also be used to implement fault-tolerance by executing code redundantly on multiple cores in parallel. Parallel programming and redundant execution are typical examples for fork-join tasks in which the program is partially parallelized. However, complex synchronization of parallel segments across multiple cores can cause unanticipated effects. This is especially problematic in hard real-time applications where data must be available in bounded time (e.g. stereo vision for pedestrian detection). The contribution of this work is a novel worst-case response time analysis which accounts for synchronization of fork-join tasks with arbitrary deadlines. We apply the analysis to the Romain framework which extends the L4 micro kernel by redundant multithreading targeted towards fault-tolerant embedded systems. By using formal analysis, we show that parallelizing workloads can lead to drastic performance impairments compared to traditional sequential execution if not done carefully.