@conference {1134,
	title = {Power-aware Computing: Measurement, Control, and Performance Analysis for Intel Xeon Phi},
	booktitle = {2017 IEEE High Performance Extreme Computing Conference (HPEC{\textquoteright}17), Best Paper Finalist},
	year = {2017},
	month = {2017-09},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Waltham, MA},
	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 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.},
	doi = {https://doi.org/10.1109/HPEC.2017.8091085},
	author = {Azzam Haidar and Heike Jagode and Asim YarKhan and Phil Vaccaro and Stanimire Tomov and Jack Dongarra}
}
