TY - GEN
T1 - Power-aware computing
T2 - 2017 IEEE High Performance Extreme Computing Conference, HPEC 2017
AU - Haidar, Azzam
AU - Jagode, Heike
AU - Yarkhan, Asim
AU - Vaccaro, Phil
AU - Tomov, Stanimire
AU - Dongarra, Jack
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/10/30
Y1 - 2017/10/30
N2 - 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- A nd exa-scale systems. Understanding and improving the energy efficiency of numerical simulation becomes very crucial. We present a detailed study and investigation toward controlling power usage and exploring how different power caps affect the performance of numerical algorithms with different computational 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.
AB - 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- A nd exa-scale systems. Understanding and improving the energy efficiency of numerical simulation becomes very crucial. We present a detailed study and investigation toward controlling power usage and exploring how different power caps affect the performance of numerical algorithms with different computational 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.
UR - http://www.scopus.com/inward/record.url?scp=85041175142&partnerID=8YFLogxK
U2 - 10.1109/HPEC.2017.8091085
DO - 10.1109/HPEC.2017.8091085
M3 - Conference contribution
AN - SCOPUS:85041175142
T3 - 2017 IEEE High Performance Extreme Computing Conference, HPEC 2017
BT - 2017 IEEE High Performance Extreme Computing Conference, HPEC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 September 2017 through 14 September 2017
ER -
