%0 Journal Article %J Concurrency Computation: Practice and Experience %D 2018 %T Investigating Power Capping toward Energy-Efficient Scientific Applications %A Azzam Haidar %A Heike Jagode %A Phil Vaccaro %A Asim YarKhan %A Stanimire Tomov %A Jack Dongarra %K energy efficiency %K High Performance Computing %K Intel Xeon Phi %K Knights landing %K papi %K performance analysis %K Performance Counters %K power efficiency %X The emergence of power efficiency as a primary constraint in processor and system design 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, which may house petascale or exascale-level computing systems. At these extreme scales, understanding and improving the energy efficiency of numerical libraries and their related applications becomes a crucial part of the successful implementation and operation of the computing system. In this paper, we study and investigate the practice of controlling a compute system's power usage, and we explore how different power caps affect the performance of numerical algorithms with different computational intensities. Further, we determine the impact, in terms of performance and energy usage, that these caps have on a system running scientific applications. This analysis will enable us to characterize the types of algorithms that benefit most from these power management schemes. Our experiments are performed using a set of representative kernels and several popular scientific benchmarks. We quantify a number of power and performance measurements and draw observations and conclusions that can be viewed as a roadmap to achieving energy efficiency in the design and execution of scientific algorithms. %B Concurrency Computation: Practice and Experience %V 2018 %P 1-14 %8 2018-04 %G eng %U https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.4485 %N e4485 %R https://doi.org/10.1002/cpe.4485 %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 %0 Generic %D 2017 %T Power-Aware HPC on Intel Xeon Phi KNL Processors %A Azzam Haidar %A Heike Jagode %A Asim YarKhan %A Phil Vaccaro %A Stanimire Tomov %A Jack Dongarra %I ISC High Performance (ISC17), Intel Booth Presentation %C Frankfurt, Germany %8 2017-06 %G eng