Accelerating Full-System Simulation through Characterizing and Predicting Operating System Performance ∗

The ongoing trend of increasing computer hardware and software complexity has resulted in the increase in complexity and overheads of cycle-accurate processor system simulation, especially in full-system simulation which not only simulates user applications, but also the Operating System (OS) and system libraries. This paper seeks to ad-dress how to accelerate full-system simulation through studying, char-acterizing, and predicting the performance behavior of OS services. Through studying the performance behavior of OS services, we found that each OS service exhibits multiple but limited behav-ior points that are repeated frequently. OS services also exhibit application-specific performance behavior and largely irregular pat-terns of occurrences. We exploit the observation to speed up full sys-tem simulation. A simulation run is divided into two non-overlapping periods: a learning period in which performance behavior of instances of an OS service are characterized and recorded, and a prediction pe-riod in which detailed simulation is replaced with a much faster emu-lation mode. During a prediction period, the behavior signature of an instance of an OS service is obtained through emulation while perfor-mance of the instance is predicted based on its signature and records of the OS service’s past performance behavior. Statistically-rigorous algorithms are used to determine when to switch between learning and prediction periods. We test our simulation acceleration method with a set of OS-intensive applications and a recent version of Linux OS running on top of a detailed processor and memory hierarchy model implemented on Simics, a popular full-system simulator. On average, the method needs the learning periods to cover only 11 % of OS service invoca-tions in order to produce highly accurate performance estimates. This leads to an estimated simulation speedup of 4.9×, with an average performance prediction error of only 3.2%, and a worst case error of 4.2%.