Supertask: Maximizing Runnable-level Parallelism in AUTOSAR Applications

Abstract

The migration of legacy AUTOSAR automotive software from a single-core ECU to a multicore ECU faces two main challenges: 1) data dependencies between AUTOSAR runnables must be respected, which may limit the level of parallelism; 2) the original data-flow from the single-core must be reproduced, in order to guarantee the same functional behaviour without exhaustive validation and testing efforts afterwards. This article proposes the concept of supertask that maximizes the level of parallelism among runnables and maintains the original data-flow from the single-core. Supertasks group consecutively scheduled AUTOSAR tasks into a unique scheduling entity with a period equal to the least common multiple of tasks composing it. We evaluate supertasks with a real automotive application and compare it with existing state-of-the-art approaches with the same objectives. Our results show that supertasks effectively increase the performance with respect to current state-of-the-art, resulting in an overall performance improvement of the application when combining supertask with current approaches.