|Title||Power-aware Computing: Measurement, Control, and Performance Analysis for Intel Xeon Phi|
|Publication Type||Conference Paper|
|Year of Publication||2017|
|Authors||Haidar, A., H. Jagode, A. YarKhan, P. Vaccaro, S. Tomov, and J. Dongarra|
|Conference Name||2017 IEEE High Performance Extreme Computing Conference (HPEC'17), Best Paper Finalist|
|Conference Location||Waltham, MA|
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.
Power-aware Computing: Measurement, Control, and Performance Analysis for Intel Xeon Phi
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