Hauptseite > Publikationsdatenbank > Performance measurement and analysis tools for extremely scalable systems > print

001     9822
005     20250314084057.0
024 7 _ |2 DOI
|a 10.1002/cpe.1585
024 7 _ |2 WOS
|a WOS:000283958700003
037 _ _ |a PreJuSER-9822
041 _ _ |a eng
082 _ _ |a 004
084 _ _ |2 WoS
|a Computer Science, Software Engineering
084 _ _ |2 WoS
|a Computer Science, Theory & Methods
100 1 _ |a Mohr, B.
|b 0
|u FZJ
|0 P:(DE-Juel1)132199
245 _ _ |a Performance measurement and analysis tools for extremely scalable systems
260 _ _ |a Chichester
|b Wiley
|c 2010
300 _ _ |a 2212 - 2229
336 7 _ |a Journal Article
|0 PUB:(DE-HGF)16
|2 PUB:(DE-HGF)
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|0 0
|2 EndNote
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a article
|2 DRIVER
440 _ 0 |a Concurrency and Computation: Practice and Experience
|x 1532-0626
|0 17301
|y 16
|v 22
500 _ _ |a Record converted from VDB: 12.11.2012
520 _ _ |a High-performance computing systems continue to employ more and more processor cores. Current typical high-end machines in industry, university, and government research laboratory computing centers feature thousands of computing cores. While these machines promise ever more compute power and memory capacity to tackle today's complex simulation problems, they force application developers to greatly enhance the scalability of their codes to be able to exploit it. To better support them in their porting and tuning process, many parallel-tools research groups have already started to work on scaling their methods, techniques, and tools to extreme processor counts. In this paper, we survey existing profiling and tracing tools, report on our experience in using them in extreme scaling environments, review working and promising new methods and techniques, and discuss strategies for solving open issues and problems. Copyright (C) 2010 John Wiley & Sons, Ltd.
536 _ _ |2 G:(DE-HGF)
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|c FUEK411
|a Scientific Computing (FUEK411)
536 _ _ |a 411 - Computational Science and Mathematical Methods (POF2-411)
|0 G:(DE-HGF)POF2-411
|c POF2-411
|x 1
|f POF II
536 _ _ |0 G:(DE-Juel-1)ATMLPP
|a ATMLPP - ATML Parallel Performance (ATMLPP)
|c ATMLPP
|x 2
588 _ _ |a Dataset connected to Web of Science
650 _ 7 |a J
|2 WoSType
653 2 0 |2 Author
|a performance analysis
653 2 0 |2 Author
|a parallel programming
653 2 0 |2 Author
|a scalability
700 1 _ |a Wylie, B.J.N.
|b 1
|u FZJ
|0 P:(DE-Juel1)132302
700 1 _ |a Wolf, F.
|b 2
|u FZJ
|0 P:(DE-Juel1)VDB1927
773 _ _ |a 10.1002/cpe.1585
|g Vol. 22, p. 2212 - 2229
|p 2212 - 2229
|q 22<2212 - 2229
|0 PERI:(DE-600)2052606-4
|t Concurrency and computation
|v 22
|y 2010
|x 1532-0626
856 7 _ |u http://dx.doi.org/10.1002/cpe.1585
909 C O |o oai:juser.fz-juelich.de:9822
|p VDB
913 2 _ |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
|x 0
913 1 _ |a DE-HGF
|b Schlüsseltechnologien
|l Supercomputing
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|v Computational Science and Mathematical Methods
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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
|k JSC
|l Jülich Supercomputing Centre
|g JSC
|x 0
920 1 _ |0 I:(DE-82)080012_20140620
|k JARA-HPC
|l Jülich Aachen Research Alliance - High-Performance Computing
|g JARA
|x 1
970 _ _ |a VDB:(DE-Juel1)119915
980 _ _ |a VDB
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980 _ _ |a journal
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980 _ _ |a I:(DE-82)080012_20140620
980 _ _ |a UNRESTRICTED
981 _ _ |a I:(DE-Juel1)VDB1346

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