DocumentCode
162494
Title
A power modelling approach for many-core architectures
Author
Zhiquan Lai ; King Tin Lam ; Cho-Li Wang ; Jinshu Su
Author_Institution
Nat. Key Lab. of Parallel & Distrib. Process. (PDL), Changsha, China
fYear
2014
fDate
27-29 Aug. 2014
Firstpage
128
Lastpage
132
Abstract
Many-core architectures are playing an important role in the HPC systems. But they are giving high performance at the cost of a great electrical power consumption. On Tianhe-2 supercomputer, the Xeon Phi many-core processors contribute nearly 80% of the system power. Power models are important to guide the design of dynamic power management (DPM) algorithms by predicting the power consumption with respect to power states and program execution patterns. However, the complexity of many-core hardware design makes power modelling be a challenging work. These concerns lead us to try a power modelling approach for many-core architectures based on the performance monitoring counters (PMC). The key insight is based on a large number of micro benchmarks on a real many-core platform, where we find some essential rules determining the chip power. Following the modelling approach, we develop an accurate chip power model for the Intel SCC many-core chip. Experimental comparison shows that our model is much more accurate than others.
Keywords
multiprocessing systems; parallel processing; power consumption; power engineering computing; HPC systems; Intel SCC many-core chip; Tianhe-2 supercomputer; Xeon Phi many-core processors; dynamic power management algorithms; electrical power consumption; many-core architectures; many-core hardware design; performance monitoring counters; power modelling approach; power models; program execution patterns; system power; Computational modeling; Computer architecture; Educational institutions; Frequency measurement; Power demand; Power measurement; Semiconductor device measurement; many-core; model; power management; power modelling;
fLanguage
English
Publisher
ieee
Conference_Titel
Semantics, Knowledge and Grids (SKG), 2014 10th International Conference on
Conference_Location
Beijing
Type
conf
DOI
10.1109/SKG.2014.10
Filename
6964677
Link To Document