Adaptive estimation and prediction of power and performance in high performance computing

Power consumption has become an increasingly important constraint in high-performancecomputing systems, shifting the focus from peak performance towards improving power efficiency. This has resulted in significant research on reducing and managing power consumption. To have an effective power management system in place, it is essential to model and estimate the runtime power of a computing system. Performance monitoring counters (PMCs) along with regression methods are commonly used in this regard to model and estimate the runtime power. However, architectural intuitions remain fundamental with regards to the current models that relate a computing system’s power to its PMCs. By employing an orthogonal approach, we examine the relationship between power and PMCs from a stochastic perspective. In this paper, we argue that autoregressive moving average (ARMA) models are excellent candidates for modeling various trends in performance and power. ARMA models focus on a time series perspe...

• Type: Journal
• Year: 2010
• Where: IFE
• Authors: Reza Zamani, Ahmad Afsahi
• Keywords: IFE 2010, PMCs, Power Consumption, Runtime Power