Optimal Implementation of Simulink Models on Multicore Architectures with Partitioned Fixed Priority Scheduling

Model-based design based on the Simulink modeling formalism and the associated toolchain has gained its popularity in the development of complex embedded control systems. However,the current research on software synthesis for Simulink models has a critical gap for providing a deterministic, semantics-preserving implementation on multicore architectures with partitioned fixed-priority scheduling. In this thesis, we propose to judiciously assign task offset, task priority, and task communication mechanism, to avoid simultaneous access to shared memory by tasks on different cores, to preserve the model semantics, and to optimize the control performance. We develop two approaches to solve the problem: (a) a mixed integer linear programming (MILP) formulation; and (b) a problem specific exact algorithm that may run several magnitudes faster than MILP.