We present the first end-to-end modeling and compilation flow to parallelize hard real-time control applications while fully guaranteeing the respect of real-time requirements on off-the-shelf hardware. It scales to thousands of dataflow nodes and has been validated on two production avionics applications. Unlike classical optimizing compilation, it takes as input non-functional requirements (real time, resource limits). To enforce these requirements, the compiler follows a static resource allocation strategy, from coarse-grain tasks communicating over an interconnection network all the way to individual variables and memory accesses. It controls timing interferences resulting from mapping decisions in a precise, safe, and scalable way.

Embedded systems tend to require more and more computational power. Many-core architectures are good candidates since they offer power and are considered more time predictable than classical multi-cores. Data-flow Synchronous languages such as Lustre or Scade are widely used for avionic critical software. Programs are described by networks of computational nodes. Implementation of such programs on a many-core architecture must ensure a bounded response time and preserve the functional behavior by taking interference into account. We consider the top-level node of a Lustre application as a software architecture description where each sub-node corresponds to a potential parallel task. Given a mapping (tasks to cores), we automatically generate code suitable for the targeted many-core architecture. This minimizes memory interferences and allows usage of a framework to compute the Worst-Case Response Time.