Worst-Case Execution-Time-Aware Parallelization of Model-Based Avionics Applications

Abstract

Multicore processing systems are the solution of choice to provide high embedded computing performance, but drawbacks in timing predictability and programmability limit their adoption in safety-critical aerospace applications. This work presents a compiler tool flow for automated parallelization of model-based real-time software, which addresses the shortcomings of multicore architectures in real-time systems. The flow is demonstrated using a model-based terrain awareness and warning systems (TAWSs) and an edge detection algorithm from the image-processing domain. Model-based applications are first transformed into real-time C code and, from there, into a well-predictable parallel C program. Tight bounds for the worst-case execution time (WCET) of the parallelized program can be determined using an integrated multicore WCET analysis. Thanks to the use of an architecture description language, the general approach is applicable to a wider range of target platforms. An experimental evaluation for a research architecture with network-on-chip interconnect shows that the parallel WCET of the TAWS application can be improved by a factor of 1.77 using the presented compiler tools.