Watt Watcher: Fine-Grained Power Estimation for Emerging Workloads

Abstract

Extensive research has focused on estimating power to guide advances in power management schemes, thermal hot spots, and voltage noise. However, simulated power models are slow and struggle with deep software stacks, while direct measurements are typically coarse-grained. This paper introduces Watt Watcher, a multicore power measurement framework that offers fine-grained functional unit breakdowns. Watt Watcher operates by passing event counts and a hardware descriptor file into configurable back-end power models based on McPAT. Researchers and vendors can add other processors to our tool by mapping to the Watt Watcher interface. We show that Watt Watcher, when calibrated, has a MAPE (mean absolute percentage error) of 2.67% aggregated over all benchmarks when compared to measured power consumption on SPEC CPU 2006 and multithreaded PARSEC benchmarks across three different machines of various form factors and manufacturing processes. We present two use cases showing how Watt Watcher can derive insights that are difficult to obtain through other measurement infrastructures. Additionally, we illustrate how Watt Watcher can be used to provide insights into challenging big data and cloud workloads on a server CPU. Through the use of Watt Watcher, it is possible to obtain a detailed power breakdown on real hardware without vendor proprietary models or hardware instrumentation.