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System-level design space exploration (DSE), which is performed early in the design process, is of eminent importance to the design of complex multi-processor embedded system architectures. During system-level DSE, system parameters like, e.g., the number and type of processors, the type and size of memories, or the mapping of application tasks to architectural resources, are considered. Simulation-based DSE, in which different design instances are evaluated using system-level simulations, typically are computationally costly. Even using high-level simulations and efficient exploration algorithms, the simulation time to evaluate design points forms a real bottleneck in such DSE. Therefore, the vast design space that needs to be searched requires effective design space pruning techniques. This paper presents a technique to reduce the number of simulations needed during system-level DSE. More specifically, we propose an iterative design space pruning methodology based on static throughput analysis of different application mappings. By interleaving these analytical throughput estimations with simulations, our hybrid approach can significantly reduce the number of simulations that are needed during the process of DSE.