001     9826
005     20250314084057.0
024 7 _ |2 DOI
|a 10.1002/cpe.1556
024 7 _ |2 WOS
|a WOS:000276682000003
024 7 _ |a altmetric:9118070
|2 altmetric
037 _ _ |a PreJuSER-9826
041 _ _ |a eng
082 _ _ |a 004
084 _ _ |2 WoS
|a Computer Science, Software Engineering
084 _ _ |2 WoS
|a Computer Science, Theory & Methods
100 1 _ |0 P:(DE-Juel1)132112
|a Geimer, M.
|b 0
|u FZJ
245 _ _ |a The Scalasca performance toolset architecture
260 _ _ |a Chichester
|b Wiley
|c 2010
300 _ _ |a 702 - 719
336 7 _ |a Journal Article
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336 7 _ |a ARTICLE
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336 7 _ |a JOURNAL_ARTICLE
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336 7 _ |a article
|2 DRIVER
440 _ 0 |0 17301
|a Concurrency and Computation: Practice and Experience
|v 22
|x 1532-0626
|y 6
500 _ _ |a Contract/grant sponsor: Helmholtz Association; contract/grant numbers: VH-NG-118, VH-VI-228Contract/grant sponsor: German Federal Ministry of Research and Education (BMBF); contract/grant number: 01IS07005C
520 _ _ |a Scalasca is a performance toolset that has been specifically designed to analyze parallel application execution behavior on large-scale systems with many thousands of processors. It offers an incremental performance-analysis procedure that integrates runtime summaries with in-depth studies of concurrent behavior via event tracing, adopting a strategy of successively refined measurement configurations. Distinctive features are its ability to identify wait states in applications with very large numbers of processes and to combine these with efficiently summarized local measurements. In this article, we review the current toolset architecture, emphasizing its scalable design and the role of the different components in transforming raw measurement data into knowledge of application execution behavior. The scalability and effectiveness of Scalasca are then surveyed from experience measuring and analyzing real-world applications on a range of computer systems. Copyright (C) 2010 John Wiley & Sons, Ltd.
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|a ATMLPP - ATML Parallel Performance (ATMLPP)
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588 _ _ |a Dataset connected to Web of Science
650 _ 7 |2 WoSType
|a J
653 2 0 |2 Author
|a parallel computing
653 2 0 |2 Author
|a performance analysis
653 2 0 |2 Author
|a scalability
700 1 _ |0 P:(DE-Juel1)VDB1927
|a Wolf, F.
|b 1
|u FZJ
700 1 _ |0 P:(DE-Juel1)132302
|a Wylie, B.
|b 2
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700 1 _ |0 P:(DE-Juel1)VDB92350
|a Ábrahám, E.
|b 3
|u FZJ
700 1 _ |0 P:(DE-Juel1)VDB62975
|a Becker, D.
|b 4
|u FZJ
700 1 _ |0 P:(DE-Juel1)132199
|a Mohr, B.
|b 5
|u FZJ
773 _ _ |0 PERI:(DE-600)2052606-4
|a 10.1002/cpe.1556
|g p. 702 - 719
|p 702 - 719
|q 702 - 719
|t Concurrency and computation
|x 1532-0626
|y 2010
856 7 _ |u http://dx.doi.org/10.1002/cpe.1556
909 C O |o oai:juser.fz-juelich.de:9826
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914 1 _ |y 2010
915 _ _ |0 StatID:(DE-HGF)0010
|a JCR/ISI refereed
920 1 _ |0 I:(DE-Juel1)JSC-20090406
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920 1 _ |0 I:(DE-82)080012_20140620
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|l Jülich Aachen Research Alliance - High-Performance Computing
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