Cita:
Carretero Pérez, Jesús; et.al. (eds.). (2015) Proceedings of the Second International Workshop on Sustainable Ultrascale Computing Systems (NESUS 2015): Krakow, Poland. Universidad Carlos III de Madrid, pp. 107-116.
ISBN:
978-84-608-2581-4
Agradecimientos:
The work presented in this paper has been partially supported by the EU Project FP7 318793 “EXA2GREEN”
and partially supported by the EU under the COST Programme Action IC1305, “Network for Sustainable Ultrascale Computing (NESUS)” and by the grant TIN2013-41350-P, Scalable Data Management Techniques for High-End Computing Systems from the Spanish Ministry of Economy and Competitiveness. European Community's Seventh Framework Program
Proyecto:
Gobierno de España. TIN2013-41350-P info:eu-repo/grantAgreement/EC/FP7/318793
Palabras clave:
HPC
,
I/O operations
,
Power analysis
,
System metrics
,
Statistical analysis
Data movement is becoming a key issue in terms of performance and energy consumption in high performance computing (HPC) systems, in general, and Exascale systems, in particular. A preliminary step to perform I/O optimization and face the Exascale challenges iData movement is becoming a key issue in terms of performance and energy consumption in high performance computing (HPC) systems, in general, and Exascale systems, in particular. A preliminary step to perform I/O optimization and face the Exascale challenges is to deepen our understanding of energy consumption across the I/O stacks. In this paper, we analyze the power draw of different I/O operations using a new fine-grained internal wattmeter while simultaneously collecting system metrics. Based on correlations between the recorded metrics and the instantaneous internal power consumption, our methodology identifies the significant metrics with respect to power consumption and decides which ones should contribute directly or in a derivative manner. This approach has the advantage of building I/O power models based on a previous set of identified utilization metrics. This technique will be validated using write operations on an Intel Xeon Nehalem server system, as writes exhibit interesting patterns and distinct power regimes.[+][-]