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| 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 |
| Date Published | 2017-09 |
| Publisher | IEEE |
| Conference Location | 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 | 10.1109/HPEC.2017.8091085 |
