Refine
Document Type
Language
- English (3)
Has Fulltext
- no (3)
Is part of the Bibliography
- no (3)
Keywords
With multi-core controllers entering the area of automotive control ECUs, strategies for parallelizing the control- algorithms come into focus. This paper deals with a special part of automotive powertrain software, called state transitions. Since dependencies between runnables executed there are weak, the transitions provide a good basis for parallelization. We present a strategy of how to distribute efficiently the execution of runnables to different cores while taking care of inner and outer dependencies. The strategy is accompanied by two case studies demonstrating the performance of the concept. The first one is carried out to find the most efficient strategies of parallelize state transitions based on randomly generated, simulated state transitions. In the second one, the developed partitioning strategies are applied to a real software project for an automotive powertrain system.
Towards the Derivation of Guidelines for the Deployment of Real-Time Tasks on a Multicore Processor
(2014)
The deployment of automotive software on a multicore processor includes the task of mapping executables to cores. Given the number of possible solutions, integrators have to solve a complex problem. Considering multiple, often conflicting goals like minimizing task response times and memory consumption, complexity further increased with the advent of multicore processors. We present a model-based approach for deriving design rules supporting integrators with statically mapping tasks to a multicore ECU. First, an evolutionary algorithm is used to sample the design space. For each sample, a model-based analysis is performed, resulting in the required fitness values according to the system metric objectives. Finally, subsets of the sample population are used to derive deployment guidelines by evaluating similarities between highly ranked solutions. This reduces the number of solutions to be considered by the integrators by orders of magnitude. In a case-study, we demonstrate the developed approach on an artificial automotive engine management system.