001     152042
005     20250314084110.0
020 _ _ |a 978-1-61499-380-3
024 7 _ |a 10.3233/978-1-61499-381-0-783
|2 doi
024 7 _ |a WOS:000452120400079
|2 WOS
037 _ _ |a FZJ-2014-01862
100 1 _ |a Jaeger, Julien
|0 P:(DE-HGF)0
|b 0
|e Corresponding Author
111 2 _ |a International Conference on Parallel Computing
|g ParCo 2013
|c Munich
|d 2013-09-10 - 2013-09-13
|w Germany
245 _ _ |a Binary Instrumentation for Scalable Performance Measurement of OpenMP Applications
260 _ _ |c 2014
|b IOS Press
295 1 0 |a Parallel Computing: Accelerating Computational Science and Engineering (CSE)
300 _ _ |a 783 - 792
336 7 _ |a Contribution to a conference proceedings
|b contrib
|m contrib
|0 PUB:(DE-HGF)8
|s 1396418051_15498
|2 PUB:(DE-HGF)
336 7 _ |a Contribution to a book
|0 PUB:(DE-HGF)7
|2 PUB:(DE-HGF)
|m contb
336 7 _ |a Conference Paper
|0 33
|2 EndNote
336 7 _ |a CONFERENCE_PAPER
|2 ORCID
336 7 _ |a Output Types/Conference Paper
|2 DataCite
336 7 _ |a conferenceObject
|2 DRIVER
336 7 _ |a INPROCEEDINGS
|2 BibTeX
490 0 _ |a Advances in Parallel Computing
|v 25
520 _ _ |a In this paper we present a binary instrumentation methodology to monitor runtime events. We demonstrate our approach on OpenMP constructs for the Intel and GNU compilers. A binary-level static analysis detects the compiler patterns and the runtime function calls corresponding to OpenMP regions. To this effect we integrate the software tool MAQAO with the scalable measurement infrastructure Score-P. We design a new interface and modify both tools to support the new events. The main advantages of using binary instrumentation are the possibility to retrieve implicit runtime events, to instrument without recompilation, to be independent from the language, and not to interact with compiler optimization. Our validation experiments and first results shows that binary instrumentation has not introduced any additional overhead.
536 _ _ |a 411 - Computational Science and Mathematical Methods (POF2-411)
|0 G:(DE-HGF)POF2-411
|c POF2-411
|f POF II
|x 0
536 _ _ |0 G:(DE-Juel-1)ATMLPP
|a ATMLPP - ATML Parallel Performance (ATMLPP)
|c ATMLPP
|x 1
700 1 _ |a Philippen, Peter
|0 P:(DE-Juel1)143710
|b 1
|u fzj
700 1 _ |a Petit, Eric
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Rubial, Andres Charif
|0 P:(DE-HGF)0
|b 3
700 1 _ |a Rössel, Christian
|0 P:(DE-Juel1)132244
|b 4
|u fzj
700 1 _ |a Jalby, William
|0 P:(DE-HGF)0
|b 5
700 1 _ |a Mohr, Bernd
|0 P:(DE-Juel1)132199
|b 6
|u fzj
773 _ _ |a 10.3233/978-1-61499-381-0-783
909 C O |o oai:juser.fz-juelich.de:152042
|p VDB
910 1 _ |a Forschungszentrum Jülich GmbH
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)143710
910 1 _ |a Forschungszentrum Jülich GmbH
|0 I:(DE-588b)5008462-8
|k FZJ
|b 4
|6 P:(DE-Juel1)132244
910 1 _ |a Forschungszentrum Jülich GmbH
|0 I:(DE-588b)5008462-8
|k FZJ
|b 6
|6 P:(DE-Juel1)132199
913 2 _ |a DE-HGF
|b Key Technologies
|l Supercomputing & Big Data
|1 G:(DE-HGF)POF3-510
|0 G:(DE-HGF)POF3-511
|2 G:(DE-HGF)POF3-500
|v Computational Science and Mathematical Methods
|x 0
913 1 _ |a DE-HGF
|b Schlüsseltechnologien
|l Supercomputing
|1 G:(DE-HGF)POF2-410
|0 G:(DE-HGF)POF2-411
|2 G:(DE-HGF)POF2-400
|v Computational Science and Mathematical Methods
|x 0
|4 G:(DE-HGF)POF
|3 G:(DE-HGF)POF2
914 1 _ |y 2014
920 1 _ |0 I:(DE-Juel1)JSC-20090406
|k JSC
|l Jülich Supercomputing Center
|x 0
980 _ _ |a contrib
980 _ _ |a VDB
980 _ _ |a contb
980 _ _ |a I:(DE-Juel1)JSC-20090406
980 _ _ |a UNRESTRICTED


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21