Automatic parallelization of simulink models for multi-core architectures

This paper addresses the problem of parallelizing existing single-rate Simulink models for embedded control applications on multi-core architectures considering communication cost between blocks on different CPU cores. Utilizing the block diagram of the Simulink model, we derive the dependency graph between the different blocks. In order to solve the scheduling problem, we describe a Mixed Integer Linear Programming (MILP) formulation for optimally mapping the Simulink blocks to different CPU cores. Since the number of variables and constraints for MILP solver grows exponentially when model size increases, solving this problem in a reasonable time becomes harder. For addressing this issue, we introduce a set of techniques for reducing the number of constraints in the MILP formulation. By using the proposed techniques, the MILP solver finds solutions that are closer to the optimal solution within a given time bound. We study the scalability and efficiency of our consisting approach with synthetic benchmarks of randomly generated directed acyclic graphs. We also use the 'Fault-Tolerant Fuel Control System' demo from Simulink and a Diesel engine controller from Toyota as case studies for demonstrating applicability of our approach to real world problems.