Statistical Performance Modeling in Functional Instruction Set Simulators
Despite the recent progress in improving the speed of instruction-accurate simulators cycle-accurate simulation is still prohibitively slow for all but the most basic programs. In this article we present a statistical machine learning approach to performance estimation in fast, instruction accurate simulators and evaluate our methodology comprehensively against three popular embedded RISC processors and about 300 embedded applications. We show that our methodology is capable of providing accurate performance estimations with an average error of less than 3.9% while, on average, operating approximate to 14.5 times faster than cycle-accurate simulation.
statistical performance modeling performance instruction set simulator cycle-accurate simulator experimentation measurement