Scale-Model Architectural Simulation

Abstract

Computer architects extensively use simulation to steer future processor research and development. Simulating large-scale multicore processors is extremely time-consuming and is sometimes impossible because of simulation infrastructure constraints and/or simulation host compute and memory limitations. This paper proposes scale-model simulation, a novel methodology to predict large-scale multicore system performance. Scale-model simulation first constructs and simulates a scale model of the target system with reduced core count and shared resources. Target system performance is then predicted through machine-learning (ML) based extrapolation. Scale-model simulation predicts 32-core target system performance based on a single-core scale model with an average error of 8.0% and 15.8% for homogeneous and heterogeneous multiprogram workloads, respectively, while yielding a $28\times$ simulation speedup.