Multi-Processor Systems-on-Chips (MPSoCs) are becoming increasingly complex, and mapping and
scheduling of multi-task applications on computational
units is key to meeting performance constraints and
power budgets. Abstract models of system components
and deployment of advanced algorithmic techniques for
the optimization problem can provide for fast design
space exploration and for optimal solutions. In [3] we
have proposed an efficient hybrid approach for solving
the problem based on an integrated Constraint Programming (CP) and Integer Programming (IP)
solution where IP performs the allocation and CP the
scheduling. The hybrid solution exploits problem decomposition and chooses for each subproblem the best
algorithm for solving it given its structure. We have
proved the efficiency of the hybrid approach w.r.t. both
stand alone CP and IP approaches. In this paper, we
go a step further and exploit an accurate MPSoC virtual platform for capturing mismatches between
problem formulation and real-life systems, and for assessing their impact on expected performance. We
therefore evaluate the efficiency and the executability of the
solution found by the algorithm using a MPSoCs platform simulator.
