Scale-Model Simulation

Abstract

Computer architects extensively use simulation to steer future processor development and research. 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. Configuring the scale model (i.e., changing core count while proportionally scaling the shared resources) enables trading off accuracy versus simulation speed. For a 32-core target system running multiprogram workloads, configuring the scale model for the highest simulation speedup of 28× yields an average absolute prediction error of 6%. Configuring the scale model for highest accuracy brings down the prediction error to 2.7%, while still delivering a 2.6× simulation speedup. ! INTRODUCTIONPredicting performance for a future computer system is a challenging and critical problem. The traditional approach is to employ detailed architectural simulation. Unfortunately, simulation is extremely time-consuming. In addition, simulation infrastructures have their limitations and may not be able to simulate a future large-scale system because of excessive memory consumption or insufficient compute capabilities in the simulation host system when simulating large numbers of cores. Researchers and practitioners employ a variety of techniques to tackle the simulation challenge. A widely used solution is sampled simulation [1], [2]. Unfortunately, this approach does not solve the simulation problem when it comes to simulating increasingly large target systems. In particular, we find that simulating an 8-core, 16-core and 32-core target system using Sniper [3], a fast and state-of-the-art parallel multicore simulator, takes 8, 17 and 43 hours, respectively, on a powerful 36-core simulation host when running multiprogram SPEC CPU workloads with (only) one billion instructions per benchmark. The super-linear increase in simulation time and complexity as a function of system size is a major challenge for computer architects in academia and industry.In this paper, we propose scale-model simulation, a novel paradigm to predict future system performance. Scale-model simulation combines architectural simulation with machine learning to predict performance for large-scale systems based on detailed simulation of a scaled-down configuration of the target system, called the scale model. Scale model simulation first simulates a scale model of the target system. Performance for the target system is then predicted through extrapolation. Scale models solve the two problems aforementioned: (1) scale models speed up the simulation of large-scale systems: scale models are small enough to simulate in reasonable amount of time while performance extrapolation is i...

Scale-Model Architectural Simulation

“…In this paper, we propose scale-model simulation, a novel paradigm to predict future system performance [4]. Scalemodel simulation combines architectural simulation with machine learning to predict performance for large-scale systems based on detailed simulation of a scaled-down configuration of the target system, called the scale model.…”