Quantifying the Impact of Variability and Heterogeneity on the Energy Efficiency for a Next-Generation Ultra-Green Supercomputer

Supercomputers, nowadays, aggregate a large number of nodes featuring the same nominal HW components (e.g., processors and GPGPUS). In real-life machines, the chips populating each node are subject to a wide range of variability sources, related to performance and temperature operating points (i.e.,...

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Bibliographic Details
Published inIEEE transactions on parallel and distributed systems Vol. 29; no. 7; pp. 1575 - 1588
Main Authors Fraternali, Francesco, Bartolini, Andrea, Cavazzoni, Carlo, Benini, Luca
Format Journal Article
LanguageEnglish
Published New York IEEE 01.07.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Computer architecture
DVFS
dynamic resource management
Energy efficiency
Energy management
Energy measurement
energy-aware computing
energy-efficient software design
Frequency measurement
green supercomputer
Green500
Hardware
hardware accelerator
hardware variability
heterogeneous supercomputer
high-performance computing
Power efficiency
Power measurement
Program processors
Supercomputers
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ISSN1045-9219
1558-2183
2161-9883
1558-2183
DOI10.1109/TPDS.2017.2766151

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Summary:Supercomputers, nowadays, aggregate a large number of nodes featuring the same nominal HW components (e.g., processors and GPGPUS). In real-life machines, the chips populating each node are subject to a wide range of variability sources, related to performance and temperature operating points (i.e., ACPI p-states) as well as process variations and die binning. Eurora is a fully operational supercomputer prototype that topped July 2013 Green500 and it represents a unique 'living lab' for next-generation ultra-green supercomputers. In this paper we evaluate and quantify the impact of variability on Eurora's energy-performance tradeoffs under a wide range of workloads intensity. Our experiments demonstrate that variability comes from hardware component mismatches as well as from the interplay between run-time energy management and workload variations. Thus, variability has a significant impact on energy efficiency even at the moderate scale of the Eurora machine, thereby substantiating the critical importance of variability management in future green supercomputers.
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ISSN:1045-9219
1558-2183
2161-9883
1558-2183
DOI:10.1109/TPDS.2017.2766151