001033653 001__ 1033653
001033653 005__ 20250314084122.0
001033653 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-06525
001033653 037__ $$aFZJ-2024-06525
001033653 041__ $$aEnglish
001033653 1001_ $$0P:(DE-Juel1)190218$$aHaus, Katharina$$b0$$eCorresponding author$$ufzj
001033653 245__ $$aExtending a Performance Analysis Tool to Handle MPI Message Probing$$f2024-06-17 - 2024-08-13
001033653 260__ $$c2024
001033653 300__ $$a61 p.
001033653 3367_ $$2DRIVER$$abachelorThesis
001033653 3367_ $$02$$2EndNote$$aThesis
001033653 3367_ $$2DataCite$$aOutput Types/Supervised Student Publication
001033653 3367_ $$0PUB:(DE-HGF)2$$2PUB:(DE-HGF)$$aBachelor Thesis$$bbachelor$$mbachelor$$s1736166779_29200
001033653 3367_ $$2BibTeX$$aMASTERSTHESIS
001033653 3367_ $$2ORCID$$aSUPERVISED_STUDENT_PUBLICATION
001033653 502__ $$aBachelorarbeit, FH Aachen - University of Applied Sciences, 2024$$bBachelorarbeit$$cFH Aachen - University of Applied Sciences$$d2024$$o2024-08-20
001033653 520__ $$aIn the realm of high-performance computing (HPC), exascale systems have now reached a point of practical reality and offer unprecedented computing power. As these systems become more powerful, it is crucial to ensure that users can effectively harness this power. It is essential to consider scalability and performance optimization when developing applications running on such systems to be able to fully leverage their capabilities. Performance analysis is a fundamental aspect of optimizing parallel applications. This thesis addresses a specific gap in the Scalasca performance analysis tool: MPI message probing. Message probing is useful to determine the required buffer size for a pending message. This work presents an extended event model that captures probe calls and integrates them into Scalasca’s analysis framework. The enhanced tool is evaluated across various test cases, demonstrating its capability to identify inefficiencies related to MPI message probing, especially the Late Sender wait state. The results show that the extended analysis provides a more comprehensive insight into application behavior.
001033653 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001033653 536__ $$0G:(DE-Juel-1)ATMLPP$$aATMLPP - ATML Parallel Performance (ATMLPP)$$cATMLPP$$x1
001033653 8564_ $$uhttps://juser.fz-juelich.de/record/1033653/files/bachelorarbeit-hausKatharina.pdf$$yOpenAccess
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001033653 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)190218$$aForschungszentrum Jülich$$b0$$kFZJ
001033653 9131_ $$0G:(DE-HGF)POF4-511$$1G:(DE-HGF)POF4-510$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5112$$aDE-HGF$$bKey Technologies$$lEngineering Digital Futures – Supercomputing, Data Management and Information Security for Knowledge and Action$$vEnabling Computational- & Data-Intensive Science and Engineering$$x0
001033653 9141_ $$y2024
001033653 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
001033653 920__ $$lyes
001033653 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
001033653 980__ $$abachelor
001033653 980__ $$aVDB
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001033653 980__ $$aI:(DE-Juel1)JSC-20090406
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