Machine learning for performance and power modeling/prediction

Abstract

Effective design space exploration relies on fast and accurate pre-silicon performance and power models. Simulation is commonly used for understanding architectural tradeoffs, however many emerging workloads cannot even run on many full-system simulators. Even if you manage to run an emerging workload, it may be a tiny part of the workload, because detailed simulators are prohibitively slow. This talk presents some examples of how machine learning can be used to solve some of the problems haunting the performance evaluation field. An application for machine learning is in cross-platform performance and power prediction. If one model is slow to run real-world benchmarks/workloads, is it possible to predict/estimate its performance/power by using runs on another platform? Are there correlations that can be exploited using machine learning to make cross-platform performance and power predictions? A methodology to perform cross-platform performance/power predictions will be presented in this talk. Another application illustrating the use of machine learning to calibrate analytical power estimation models will be discussed. Yet another application for machine learning has been to create max power stressmarks. Manually developing and tuning so called stressmarks is extremely tedious and time-consumingwhile requiring an intimate understanding of the processor. In our past research, we created a framework that uses machine learning for the automated generation of stressmarks. In this talk, the methodology of the creation of automatic stressmarks will be explained. Experiments on multiple platforms validating the proposed approach will also be described.