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000842652 041__ $$aEnglish
000842652 1001_ $$0P:(DE-HGF)0$$aVierjahn, Tom$$b0$$eCorresponding author
000842652 1112_ $$a2016 IEEE 6th Symposium on Large Data Analysis and Visualization$$cBaltimore, MD$$d2016-10-23 - 2016-10-28$$gLDAV 2016$$wUSA
000842652 245__ $$aCorrelating sub-phenomena in performance data in the frequency domain
000842652 260__ $$bIEEE$$c2016
000842652 300__ $$a105-106
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000842652 520__ $$aFinding and understanding correlated performance behaviour of the individual functions of massively parallel high-performance computing (HPC) applications is a time-consuming task. In this poster, we propose filtered correlation analysis for automatically locating interdependencies in call-path performance profiles. Transforming the data into the frequency domain splits a performance phenomenon into sub-phenomena to be correlated
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000842652 588__ $$aDataset connected to CrossRef Conference
000842652 7001_ $$0P:(DE-Juel1)168253$$aHermanns, Marc-Andre$$b1$$ufzj
000842652 7001_ $$0P:(DE-Juel1)132199$$aMohr, Bernd$$b2$$ufzj
000842652 7001_ $$0P:(DE-HGF)0$$aMuller, Matthias S.$$b3
000842652 7001_ $$0P:(DE-HGF)0$$aKuhlen, Torsten W.$$b4
000842652 7001_ $$0P:(DE-HGF)0$$aHentschel, Bernd$$b5
000842652 773__ $$a10.1109/LDAV.2016.7874340
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