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 limitations. This paper… Click to show full 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 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 workloads, respectively, while yielding a 28× simulation speedup.
               
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