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Abstract : |
This paper introduces minimal subset evaluation (MSE) as a way to reduce time spent on large-structure warm-up during the fastforwarding 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 of 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. 1, |
