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000186082 0247_ $$2doi$$a10.1109/PDP.2014.112
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000186082 037__ $$aFZJ-2015-00183
000186082 1001_ $$0P:(DE-Juel1)157897$$aGschwandtner, Philipp$$b0$$eCorresponding Author
000186082 1112_ $$a2014 22nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)$$cTorino$$d2014-02-12 - 2014-02-14$$gPDP 2014$$wItaly
000186082 245__ $$aModeling CPU Energy Consumption of HPC Applications on the IBM POWER7
000186082 260__ $$bIEEE$$c2014
000186082 300__ $$a536 - 543
000186082 3367_ $$0PUB:(DE-HGF)8$$2PUB:(DE-HGF)$$aContribution to a conference proceedings$$bcontrib$$mcontrib$$s1421057011_25614
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000186082 520__ $$aEnergy consumption optimization of HPC applications inherently requires measurements for reference and comparison. However, most of today’s systems lack the necessary hardware support for power or energy measurements. Furthermore, in-band data availability is preferred for specific optimization techniques such as auto-tuning. For this reason, we present in-band energy consumption models for the IBM POWER7 processor based on hardware counters. We demonstrate that linear regression is a suitable means for modeling energy consumption, and we rely on already available, highlevelbenchmarks for training instead of self-written or handtuned micro-kernels. We compare modeling efforts for different instruction mixes caused by two compilers (GCC and IBM XL) as well as various multi-threading usage scenarios, and validate across our training benchmarks and two real-world applications. Results show mean errors of approximately 1% and overall max errors of 5.3% for GCC.
000186082 536__ $$0G:(DE-HGF)POF2-411$$a411 - Computational Science and Mathematical Methods (POF2-411)$$cPOF2-411$$fPOF II$$x0
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000186082 588__ $$aDataset connected to CrossRef Conference
000186082 7001_ $$0P:(DE-Juel1)132163$$aKnobloch, Michael$$b1$$ufzj
000186082 7001_ $$0P:(DE-Juel1)132199$$aMohr, Bernd$$b2$$ufzj
000186082 7001_ $$0P:(DE-Juel1)144441$$aPleiter, Dirk$$b3$$ufzj
000186082 7001_ $$0P:(DE-HGF)0$$aFahringer, Thomas$$b4
000186082 773__ $$a10.1109/PDP.2014.112
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000186082 9141_ $$y2014
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000186082 9132_ $$0G:(DE-HGF)POF3-511$$1G:(DE-HGF)POF3-510$$2G:(DE-HGF)POF3-500$$aDE-HGF$$bKey Technologies$$lSupercomputing & Big Data$$vComputational Science and Mathematical Methods$$x0
000186082 9131_ $$0G:(DE-HGF)POF2-411$$1G:(DE-HGF)POF2-410$$2G:(DE-HGF)POF2-400$$3G:(DE-HGF)POF2$$4G:(DE-HGF)POF$$aDE-HGF$$bSchlüsseltechnologien$$lSupercomputing$$vComputational Science and Mathematical Methods$$x0
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