Poster: FLAMBES: evolving fast performance models
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.