This paper introduces minimal subset evaluation (MSE) as a way to reduce time spent on large-structure warm-up during the fast-forwarding portion of processor simulations. Warm up is commonly used prior to full-detail simulation to avoid cold-start bias in large structures like caches and branch predictors. Unfortunately, warm up can be very time consuming, often representing 50% or more of total simulation time. Previous techniques have used the entire fast forward interval to obtain accurate warm up, which may be prohibitive for large parameter-space searches, or chosen a short but ad-hoc warm-up length that reduces simulation time but may sacrifice accuracy. MSE probabilistically determines a minimally sufficient fraction of the set (?f fast-forward transactions that must be executed for warm up to accurately produce state as it would have appeared had the entire fast forward interval been used for warm up. The paper describes the mathematical underpinnings of MSE and demonstrates its effectiveness for both single-large-sample and multiple-sample simulation styles. In our experiments, MSE yields errors of less than 1% in IPC measurements with cycle-accurate simulation, while reducing simulation times by an average factor of two or more.
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