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Developing complex technical systems requires a systematic exploration of the given design space in order to identify optimal system configurations. However, studying the effects and interactions of even a small number of system parameters often requires an extensive number of simulation runs. This in turn results in excessive runtime demands which severely hamper thorough design space explorations. In this paper, the authors present a parallel discrete event simulation scheme that enables cost- and time-efficient execution of large scale parameter studies on GPUs. In order to efficiently accommodate the stream-processing paradigm of GPUs, their parallelization scheme exploits two orthogonal levels of parallelism: External parallelism among the inherently independent simulations of a parameter study and internal parallelism among independent events within each individual simulation of a parameter study.
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