Macro-scale simulation has been advanced as one tool for application–architecture co-design to express operation of exascale systems. These simulations approximate the behavior of system components, trading off accuracy for increased evaluation speed. Application skeletons serve as the vehicle for these simulations, but they require accurately capturing the execution behavior of computation. The complexity of application codes, the heterogeneity of the platforms, and the increasing importance of simulating multiple performance metrics (e.g., execution time, energy) require new modeling techniques.

We propose flexible statistical models to increase the fidelity of application simulation at scale. We present performance model validation for several exascale mini-applications that leverage a variety of parallel programming frameworks targeting heterogeneous architectures for both time and energy performance metrics. When paired with these statistical models, application skeletons were simulated on average 12.5 times faster than the original application incurring only 6.08% error, which is 12.5% faster and 33.7% more accurate than baseline models.

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