Accurate and Efficient Regression Modeling for Microarchitectural Performance and Power Prediction
The authors propose regression modeling as an efficient approach for accurately predicting performance and power for various applications executing on any microprocessor configuration in a large microarchitectural design space. This paper addresses fundamental challenges in microarchitectural simulation cost by reducing the number of required simulations and using simulated results more effectively via statistical modeling and inference. Specifically, they derive and validate regression models for performance and power. Such models enable computationally efficient statistical inference, requiring the simulation of only 1 in 5 million points of a joint microarchitecture-application design space while achieving median error rates as low as 4.1 percent for performance and 4.3 percent for power.