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In Proceedings of the 2011 companion on High Performance Computing Networking, Storage and Analysis Companion (2011), pp. 31-32, doi:10.1145/2148600.2148617 Key: citeulike:11563285
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Large clusters and supercomputers are simulated to aid in design. Many devices, such as hard drives, are slow to simulate. Our approach is to use a genetic algorithm to fit parameters for an analytical model of a device. Fitting focuses on aggregate accuracy rather than request-level accuracy since individual request times are irrelevant in large simulations. The model is fitted to traces from a physical device or a known device-accurate model. This is done once, offline, before running the simulation. Execution of the model is fast, since it only requires a modest amount of floating point math and no event queueing. Only a few floating point numbers are needed for state. Compared to an event-driven model, this trades a little accuracy for a large gain in performance.
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