Dimensioning the heterogeneous multicluster architecture via parallelism analysis and evolutionary computing

In the near future, embedded systems containing hundreds of processing elements running multiple con-current applications will become a reality. The het-erogeneous multicluster architecture enables to cope with the challenging hardware/software requirements presented by such systems. This paper shows principles and optimization of multicluster dimensioning aiming at an appropriate distribution of applications onto clus-ters containing different types of processing elements. The approach represents an initial exploration phase efficiently finding a suitable multicluster configuration in the large design space. Hence, results should be further refined by more accurate but less time-efficient simulation-based techniques. As the starting point, a parallelism value matrix is analytically extracted describing application mappings independently on the architecture and scheduling. A genetic algorithm (GA) and a mixed-integer linear programming (MILP) ap-proach solving the dimensioning problem are introduced and compared. Both solutions use the parallelism value matrix as input. Scalability results show that the GA generates results faster and with a satisfactory quality relative to the found MILP solutions. Finally, the dimensioning approach is demonstrated for a realistic benchmark scenario