Hauptseite > Publikationsdatenbank > Correlating sub-phenomena in performance data in the frequency domain > print

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020 _ _ |a 978-1-5090-5659-0
024 7 _ |a 10.1109/LDAV.2016.7874340
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037 _ _ |a FZJ-2018-00859
041 _ _ |a English
100 1 _ |a Vierjahn, Tom
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111 2 _ |a 2016 IEEE 6th Symposium on Large Data Analysis and Visualization
|g LDAV 2016
|c Baltimore, MD
|d 2016-10-23 - 2016-10-28
|w USA
245 _ _ |a Correlating sub-phenomena in performance data in the frequency domain
260 _ _ |c 2016
|b IEEE
300 _ _ |a 105-106
336 7 _ |a CONFERENCE_PAPER
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336 7 _ |a Conference Paper
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336 7 _ |a INPROCEEDINGS
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520 _ _ |a Finding 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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700 1 _ |a Hermanns, Marc-Andre
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700 1 _ |a Mohr, Bernd
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700 1 _ |a Muller, Matthias S.
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700 1 _ |a Kuhlen, Torsten W.
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700 1 _ |a Hentschel, Bernd
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773 _ _ |a 10.1109/LDAV.2016.7874340
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