%0 Conference Paper %B 2017 IEEE High Performance Extreme Computing Conference (HPEC'17), Best Paper Finalist %D 2017 %T Power-aware Computing: Measurement, Control, and Performance Analysis for Intel Xeon Phi %A Azzam Haidar %A Heike Jagode %A Asim YarKhan %A Phil Vaccaro %A Stanimire Tomov %A Jack Dongarra %X 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 control- ling power usage and exploring how different power caps affect the performance of numerical algorithms with different computa- tional 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. %B 2017 IEEE High Performance Extreme Computing Conference (HPEC'17), Best Paper Finalist %I IEEE %C Waltham, MA %8 2017-09 %G eng %R https://doi.org/10.1109/HPEC.2017.8091085