SCFM: A Statistical Coarse-to-Fine Method to Select Cross-Microarchitecture Reliable Simulation Points

With computer microarchitectures advancing and benchmark sizes expanding, the need for agile pre-silicon performance estimation becomes increasingly crucial. SimPoint is a widely used sampling method to solve this problem, making it a promising research area. However, previous studies mainly focus on how to enhance the estimation accuracy, speedup, and usability of SimPoint, while ignoring the critical problem of cross-microarchitecture estimation reliability. We have observed that although SimPoint can provide an accurate performance estimation, it could fail in yielding reliable estimations across different microarchitectures due to the difficulties in (a) rapidly evaluating the cross-microarchitecture reliability of SimPoint and (b) effectively selecting reliable simulation points. To address this problem, we propose SCFM, a statistical coarse-to-fine method to select cross-microarchitecture reliable simulation points. The SCFM introduces two key metrics: the micro-independent metric \(E_{repre}\) and micro-dependent metric Loss, to rapidly evaluate the simulation points. Our method could efficiently scan a large SimPoint parameter space by rapidly evaluating their program characteristic representation abilities and precisely assessing their cross-microarchitecture estimation capabilities. To verify the effectiveness of SCFM, we conducted thorough evaluations, configuring thirty distinct machine models to select reliable simulation points and preparing three test models to implement the verification. Experimental results demonstrate that the final-selected reliable simulation points could yield statistically accurate estimations for SPEC CPU 2006 on the test models, giving average errors of less than 1%.