Power-aware computing: Measurement, control, and performance analysis for Intel Xeon Phi

Abstract

The emergence of power efficiency as a primary constraint in processor and system designs poses new challenges concerning power and energy awareness for numerical libraries and scientific applications. Power consumption also plays a major role in the design of data centers in particular for peta- and exa-scale systems. Understanding and improving the energy efficiency of numerical simulation becomes very crucial. We present a detailed study and investigation toward controlling power usage and exploring how different power caps affect the performance of numerical algorithms with different computational intensities, and determine the impact and correlation with performance of scientific applications. Our analyses is performed using a set of representatives kernels, as well as many highly used scientific benchmarks. We quantify a number of power and performance measurements, and draw observations and conclusions that can be viewed as a roadmap toward achieving energy efficiency computing algorithms.