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 address how to accelerate full-system simulation through studying, characterizing, 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 behavior points that are repeated frequently. OS services also exhibit application-specific performance behavior and largely irregular patterns of occurrences. We exploit the observation to speed up full system 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 period in which detailed simulation is replaced with a much faster emulation mode. During a prediction period, the behavior signature of an instance of an OS service is obtained through emulation while performance 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 invocations in order to produce highly accurate performance estimates. This leads to an estimated simulation speedup of 4.9times, with an average performance prediction error of only 3.2%, and a worst case error of 4.2%