Hauptseite > Publikationsdatenbank > Using Directed Variance to Identify Meaningful Views in Call-path Performance Profiles > print

001     824330
005     20250314084115.0
020 _ _ |a 978-1-5090-5226-4
024 7 _ |a 10.1109/vpa.2016.7
|2 doi
037 _ _ |a FZJ-2016-06939
100 1 _ |a Vierjahn, Tom
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|e Corresponding author
111 2 _ |a The 3rd International Workshop on Visual Performance Analysis
|g VPA'16
|c Salt Lake City, Utah
|d 2016-11-18 - 2016-11-18
|w USA
245 _ _ |a Using Directed Variance to Identify Meaningful Views in Call-path Performance Profiles
260 _ _ |a Piscataway, NJ, USA
|c 2016
|b IEEE Press
295 1 0 |a Proceedings of the 3rd International Workshop on Visual Performance Analysis
300 _ _ |a 9-16
336 7 _ |a CONFERENCE_PAPER
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336 7 _ |a INPROCEEDINGS
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336 7 _ |a Contribution to a conference proceedings
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336 7 _ |a Contribution to a book
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520 _ _ |a Understanding the performance behaviour of massively parallel high-performance computing (HPC) applications based on call-path performance profiles is a time-consuming task. In this paper, we introduce the concept of directed variance in order to help analysts find performance bottlenecks in massive performance data and in the end optimize the application. According to HPC experts' requirements, our technique automatically detects severe parts in the data that expose large variation in an application's performance behaviour across system resources. Previously known variations are effectively filtered out. Analysts are thus guided through a reduced search space towards regions of interest for detailed examination in a 3D visualization. We demonstrate the effectiveness of our approach using performance data of common benchmark codes as well as from actively developed production codes.
536 _ _ |a 511 - Computational Science and Mathematical Methods (POF3-511)
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536 _ _ |a Scalable Performance Analysis of Large-Scale Parallel Applications (jzam11_20091101)
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|f Scalable Performance Analysis of Large-Scale Parallel Applications
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536 _ _ |0 G:(DE-Juel-1)ATMLPP
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700 1 _ |a Hermanns, Marc-André
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700 1 _ |a Mohr, Bernd
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700 1 _ |a Müller, 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/vpa.2016.7
909 C O |o oai:juser.fz-juelich.de:824330
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910 1 _ |a RWTH Aachen
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|v Computational Science and Mathematical Methods
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914 1 _ |y 2016
915 _ _ |a No Authors Fulltext
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920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)JSC-20090406
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