Statistically rigorous regression modeling for the microprocessor design space

Regression models enhance existing techniques in detailed microarchitectural simulation by reducing the number of required simulations and using simulation data more efficiently to identify trends and trade-offs. We present a rigorous derivation of such models for microprocessor performanceandpowerprediction, emphasizing the need to apply domain-specific knowledge when performing statistical inference. In particular, we propose sampling observations uniformly at random from a large design space, discuss approaches for identifying statistically significant predictors, and detail strategies for effectively modeling predictor interaction and non-linearity. The resulting models enable computationally efficient statistical inference, requiring the simulation of only 1 in every 5 million points of a joint microarchitecture-application design space while achieving median prediction error rates as low as 4.1 percent for performance and 4.3 percent for power.