001     893077
005     20250314084119.0
020 _ _ |a 978-3-030-66057-4
024 7 _ |a 10.1007/978-3-030-66057-4_13
|2 doi
024 7 _ |a 2128/27917
|2 Handle
037 _ _ |a FZJ-2021-02545
100 1 _ |a Knobloch, Michael
|0 P:(DE-Juel1)132163
|b 0
|e Corresponding author
111 2 _ |a Tools for High Performance Computing 2019
|c Dresden
|d 2019-09-02 - 2019-09-03
|w Fed Rep Germany
245 _ _ |a A Picture Is Worth a Thousand Numbers—Enhancing Cube’s Analysis Capabilities with Plugins
260 _ _ |a Cham
|c 2021
|b Springer International Publishing
295 1 0 |a Tools for High Performance Computing 2018 / 2019 / Mix, Hartmut (Editor) ; Cham : Springer International Publishing, 2021, Chapter 13 ; ISBN: 978-3-030-66056-7 ; doi:10.1007/978-3-030-66057-4
300 _ _ |a 237 - 259
336 7 _ |a CONFERENCE_PAPER
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336 7 _ |a Conference Paper
|0 33
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336 7 _ |a INPROCEEDINGS
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336 7 _ |a conferenceObject
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336 7 _ |a Output Types/Conference Paper
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336 7 _ |a Contribution to a conference proceedings
|b contrib
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336 7 _ |a Contribution to a book
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520 _ _ |a In the last couple of years, supercomputers became increasingly large and more and more complex. Performance analysis tools need to adapt to the system complexity in order to be used effectively at large scale. Thus, we introduced a plugin infrastructure in Cube 4, the performance report explorer for Score-P and Scalasca, which allows to extend Cube’s analysis features without modifying the source code of the GUI. In this paper we describe the Cube plugin infrastructure and show how it makes Cube a more versatile and powerful tool. We present different plugins provided by JSC that extend and enhance the CubeGUI’s analysis capabilities. These add new types of system-tree visualizations, help create reasonable filter files for Score-P and visualize simple OTF2 trace files. We also present a plugin which provides a high-level overview of the efficiency of the application and its kernels. We further discuss context-free plugins, which are used to integrate command-line Cube algebra utilities, like cube_diff and similar commands, in the GUI.
536 _ _ |a 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)
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|c POF4-511
|f POF IV
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536 _ _ |0 G:(DE-Juel-1)ATMLPP
|a ATMLPP - ATML Parallel Performance (ATMLPP)
|c ATMLPP
|x 1
588 _ _ |a Dataset connected to CrossRef Book
700 1 _ |a Saviankou, Pavel
|0 P:(DE-Juel1)132249
|b 1
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700 1 _ |a Schlütter, Marc
|0 P:(DE-Juel1)142180
|b 2
|u fzj
700 1 _ |a Visser, Anke
|0 P:(DE-Juel1)132282
|b 3
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700 1 _ |a Mohr, Bernd
|0 P:(DE-Juel1)132199
|b 4
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773 _ _ |a 10.1007/978-3-030-66057-4_13
856 4 _ |u https://juser.fz-juelich.de/record/893077/files/paper.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:893077
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910 1 _ |a Forschungszentrum Jülich
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910 1 _ |a Forschungszentrum Jülich
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910 1 _ |a Forschungszentrum Jülich
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910 1 _ |a Forschungszentrum Jülich
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910 1 _ |a Forschungszentrum Jülich
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913 1 _ |a DE-HGF
|b Key Technologies
|l Engineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action
|1 G:(DE-HGF)POF4-510
|0 G:(DE-HGF)POF4-511
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Enabling Computational- & Data-Intensive Science and Engineering
|9 G:(DE-HGF)POF4-5112
|x 0
913 0 _ |a DE-HGF
|b Key Technologies
|l Supercomputing & Big Data
|1 G:(DE-HGF)POF3-510
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|v Computational Science and Mathematical Methods
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914 1 _ |y 2021
915 _ _ |a OpenAccess
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920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)JSC-20090406
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980 _ _ |a contrib
980 _ _ |a VDB
980 _ _ |a contb
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 _ _ |a UNRESTRICTED
980 1 _ |a FullTexts


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