Executable Modelling for Highly Parallel Accelerators

High-performance embedded computing is developing rapidly since applications in most domains require a large and increasing amount of computing power. On the hardware side, this requirement is met by the introduction of heterogeneous systems, with highly parallel accelerators that are designed to take care of the computation-heavy parts of an application. There is today a plethora of accelerator architectures, including GPUs, many-cores, FPGAs, and domain-specific architectures such as AI accelerators. They all have their own programming models, which are typically complex, low-level, and involve explicit parallelism. This yields error-prone software that puts the functional safety at risk, unacceptable for safety-critical embedded applications. In this position paper we argue that high-level executable modelling languages tailored for parallel computing can help in the software design for high performance embedded applications. In particular, we consider the data-parallel model to be a suitable candidate, since it allows very abstract parallel algorithm specifications free from race conditions. Moreover, we promote the Action Language for fUML (and thereby fUML) as suitable host language.