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001008814 005__ 20250314084121.0
001008814 037__ $$aFZJ-2023-02486
001008814 041__ $$aEnglish
001008814 1001_ $$0P:(DE-Juel1)188670$$aCorbin, Gregor$$b0$$eCorresponding author$$ufzj
001008814 1112_ $$aISC High Performance '23$$cHamburg$$d2023-05-21 - 2023-05-21$$gISC '23$$wGermany
001008814 245__ $$aHands-on Practical Hybrid Parallel Application Performance Engineering
001008814 260__ $$c2023
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001008814 3367_ $$031$$2EndNote$$aGeneric
001008814 3367_ $$2BibTeX$$aMISC
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001008814 520__ $$aThis tutorial presents state-of-the-art performance tools for leading-edge HPC systems founded on the community-developed Score-P instrumentation and measurement infrastructure, demonstrating how they can be used for performance engineering of effective scientific applications based on standard MPI, OpenMP, hybrid combination of both, and increasingly common usage of accelerators. Parallel performance tools from the Virtual Institute – High Productivity Supercomputing (VI-HPS) are introduced and featured in hands-on exercises with Score-P, Scalasca, Vampir, and TAU. We present the complete workflow of performance engineering, including instrumentation, measurement (profiling and tracing, timing and PAPI hardware counters), data storage, analysis, tuning, and visualization. Emphasis is placed on how tools are used in combination for identifying performance problems and investigating optimization alternatives. Using their own notebook computers, participants will conduct exercises on a contemporary HPC system where remote access will be provided for the hands-on sessions through AWS running an E4S [http://e4s.io] image containing all of the necessary tools. This will help to prepare participants to locate and diagnose performance bottlenecks in their own parallel programs.
001008814 536__ $$0G:(DE-HGF)POF4-5112$$a5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
001008814 536__ $$0G:(EU-Grant)101033975$$aEUPEX - EUROPEAN PILOT FOR EXASCALE (101033975)$$c101033975$$fH2020-JTI-EuroHPC-2020-1$$x1
001008814 536__ $$0G:(DE-Juel-1)ATMLPP$$aATMLPP - ATML Parallel Performance (ATMLPP)$$cATMLPP$$x2
001008814 7001_ $$0P:(DE-HGF)0$$aShende$$b1
001008814 7001_ $$0P:(DE-HGF)0$$aWilliams, William$$b2
001008814 909CO $$ooai:juser.fz-juelich.de:1008814$$pec_fundedresources$$pVDB$$popenaire
001008814 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)188670$$aForschungszentrum Jülich$$b0$$kFZJ
001008814 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$aExternal Institute$$b1$$kExtern
001008814 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$$x0
001008814 9141_ $$y2023
001008814 920__ $$lno
001008814 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
001008814 980__ $$alecture
001008814 980__ $$aVDB
001008814 980__ $$aI:(DE-Juel1)JSC-20090406
001008814 980__ $$aUNRESTRICTED