Parallel Programming in Real-Time Systems

The enormous amounts of data modern real-time systems have to process lead to expensive, long-lasting calculations. In order to manage those computations in a timely manner, parallel task models have gained a lot of popularity lately. However, parallel programming can be very cumbersome and verbose. Other computationally intensive sectors have dealt with parallel computing for decades and have accumulated their experience in the development of parallel frameworks. Examples of well known parallel runtime systems are OpenMP, Intel Threading Building Blocks (TBB) and Microsoft Parallel Pattern Library (PPL). These runtime systems allow developers to enhance parallelism in their applications in a straightforward fashion. However, those parallel frameworks and the patternbased interfaces they provide might not be easily applicable in real-time systems. In this paper, we investigate the use of parallel programming frameworks in time-critical systems. On that account, we discuss considerations for the design of real-time applications that make use of such parallel runtime systems. Furthermore, we evaluate three library-based frameworks from different computing sectors, namely Intel Threading Building Blocks, Embedded Multicore Building Blocks (EMBB) and High Performance ParalleX (HPX), by conducting benchmarks of various parallel algorithms on an embedded multicore architecture.