Cache memories are one of the hardware resources with higher potential to reduce worst-case execution time (WCET) costs for software programs with tight real-time constraints. Yet, the complexity of cache analysis has caused a large fraction of real-time systems industry to avoid using them, especially in the automotive sector. For measurement-based timing analysis (MBTA) - the dominant technique in domains such as automotive - cache challenges the definition of test scenarios stressful enough to produce (cache) layouts that causing high contention. In this paper, we present our experience in enabling the use of caches for a real automotive application running on an AURIX multiprocessor, using software randomization and measurement-based probabilistic timing analysis (MBPTA). Our results show that software randomization successfully exposes - in the experiments performed for timing analysis - cache related variability, in a manner that can be effectively captured by MBPTA.

The research leading to these results has received funding from the European Community’s FP7 [FP7/2007-2013] under the PROXIMA Project (http://www.proxima project.eu), grant agreement no 611085. This work has also been partially supported by the Spanish Ministry of Science and Innovation (grant TIN2015-65316-P) and the HiPEAC Network of Excellence. Jaume Abella has been partially supported by the Ministry of Economy and Competitiveness under Ramon y Cajal fellowship RYC-2013-14717. This work was conducted as part of a collaboration with the CONCERTO project (ARTEMIS-JU grant nr. 333053), which provided the automotive application and the build automation. Authors thank Benjamin Lesage for his support in execution time collection infrastructure on the AURIX board.

Peer Reviewed