modeling superscalar processors via statistical simulation
Abstract: Statistical simulation is a technique for fast performance evaluation of superscalar processors. First, intrinsic statistical information is collected from a single detailed simulation of a program. This information is then used to generate a synthetic instruction trace that is fed to a simple processor model, along with cache and branch prediction statistics. Because of the probabilistic nature of the simulation, it quickly converges to a performance rate. The simplicity and simulation speed make it useful for fast design space exploration; as such, it is a good complement to conventional detailed simulation. The accuracy of this technique is evaluated for different levels of modeling complexity. Both errors and convergence properties are studied in detail. A simple instruction model yields an average error of 8\% compared with detailed simulation. A more detailed instruction model reduces the error to 5\% but requires about three times as long to converge.
model superscalar processor processor statistics simulation abstraction performance performance evaluation informal instruction traces cache branch prediction convergence design space accuracy complexity errors